Aulas Teóricas

T1 - Introduction to optimization.

Course introduction (evaluation, biblio., etc.). Introduction to optimization. Operations research models. Linear Programming. Formulation of LP problems.

T2 - Algorithms for LP problems

Simplex method. Interior Point method. Sensitivity analysis and Postoptimality analysis. Duality theory and sensitivity analysis.

T3 - Transportation and assignment problems.

Transportation and assignment problems formulation. problem restrictions. Transportation Simplex algorithm. Northwest corner rule. Solving Assignment problems.

T4 - Network models

Network models. Shortest path problem. Minimum spanning tree problem. Maximum flow problems. Minimum cost flow problem.

T5 - Integer Programming. Binary Integer Programming.

Integer Programming. Binary Integer Programming.
Branch and bound algorithm for BIP problems.

T6 - Solving IP problems

Algorithms for solving IP: Branch-and-bound and branch-and-cut. Constraint Programming.
Project Proposals presentation.

T7 - Nonlinear Programming

 Nonlinear Programming. Types of NP problems. Bisection and Newton methods.

T8 - Nonlinear Programing

Gradient search and Newton based Methods. KKT Conditions. Quadratic Programming. Frank-Wolfe algorithm. Nonconvex programming.

T9 - Metaheuristics

Metaheurístics: Tabu search and simulated annealing.

T10 - Metaheuristics

 Metaheurístics: Genetic Algorithms and Ant Colony Optimization, PSOs

T11 - Metaheuristics

 Metaheurístics: Ant Colony Optimization (continuation), PSOs

T12 - Dynamic programing

Dynamic Programming. Examples and characteristics of Dynamic Programming. Distribution of effort. Continuous Dynamic Programming. Probabilistic Dynamic

T13 - Decicion Theory

Introduction to Decision Analysis. Bayes decision rules. Decision Trees. Utility theory.