### Planeamento

### 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.

## 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.