todo

[djeada]

Convex Optimization

• Define convex functions & sets
• Solve a quadratic programming example
• Visualize convex vs non-convex objective functions

Global Optimization*

• Demonstrate brute-force/grid search
• Use stochastic/global methods (e.g., simulated annealing, genetic algorithms)
• Compare global vs local solutions on a multimodal function

Local Optimization

• Gradient descent on a simple function (quadratic, Rosenbrock)
• Newton’s method / quasi-Newton (BFGS)
• Visualize convergence paths starting from different initial guesses

Constrained Optimization

• Linear programming example (diet problem, resource allocation)
• Nonlinear constraints (inequalities/equalities) with Lagrange multipliers
• Use solver (e.g., scipy.optimize.minimize with constraints)
• Plot feasible region and optimum