Students are expected to have completed courses with contents in linear algebra, multivariable differential calculus, statistics, operations research, business administration and computer programming.

1. Formulating optimization models for decision-making in diverse application areas. 2. Analyzing and solving problems of nonlinear optimization and of optimization under uncertainty. 3. Using software for formulating and solving optimization models. 4. Solving optimization models through simulation.

1. Nonlinear optimization (NLO). 1.1. Introduction; examples and applications; optimization software. 1.2. Unconstrained NLO. 1.3. Equality-constrained NLO. 1.4. Inequality-constrained NLO. 1.5. NLO and machine learning. 2. Optimization under uncertainty. 2.1. Introduction to robust optimization. Formulation, solution, examples and applications. 2.2. Introduction to stochastic dynamic optimization. Formulation, solution, examples and applications. 3. Simulation-based optimization. 3.1. Monte Carlo and discrete-event simulation for optimization. Design, implementation and analysis of results.

Theory (3 ECTS). Theory classes with supporting material in Aula Global. Practice (3 ECTS). Model formulation and problem-solving classes. Computing classes. The teaching methodology will have a practical approach, being based on the formulation and solution of problems drawn from diverse application areas, both in the practical classes and in the theory classes, as motivation and illustration of the theory. There will be a weekly individual tutoring session.