The goal of this course is to become familiar with the modeling and the application of optimization methods in complex decision-making processes. In this way, we provide the necessary tools and modern techniques of optimization for the efficient solution of many decision-making problems arising in diverse areas like Business, Marketing, Finance and Engineering. In particular, the objectives are: 1. Modeling and application of optimization methods for a series of general problems (linear models, discrete models, nonlinear models and also optimization under uncertainty) 2. Learn about the basic (mathematical) foundations that support the development of solution algorithms for the optimization problems mentioned above 3. Use Python to apply tools of modern optimization techniques in an efficient way.

1. Linear Models 1.1 Examples 1.2 Properties 1.3 Algorithms 2. Discrete Models 2.1 Introduction 2.2 Logic conditions 2.3 Networks 2.4 Algorithms 3. Nonlinear Models 3.1 Examples, least squares 3.2 Optimality conditions 3.3 Algorithms 4. Uncertainty Models 4.1 Introduction and properties 4.2 Stochastic Optimization

½ lectures with supporting materials available on the Web ½ practical sessions (computer labs with Python)