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.
T6 - Solving IP problems
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.