Course description:

Researchers in Economics, Engineering and Management need a solid background on quantitative methods. Among others, dynamic optimization is a widely used tool in models within these fields.

Resource allocation, portfolio optimization, Inventory management, waste management are few but significative examples of decision making issues that have been addressed by modeling them as dynamic optimization problems. To copy with the increasing number of research papers in management science and economic literature that involves advanced mathematical tools, Ph.D. candidates should master the basic arguments of dynamic optimization theory.

The aim of the course is to make students familiar with the topics of applied research and the main assumptions used in the models to be able to apply Optimal control and Calculus of Variations tools. A background on standard real analysis, topology, linear algebra, uni- and multi-variate differential calculus and integral calculus may be necessary, although the level of skills needed can be mastered with short self study.

Learning objectives:

At the end of the course the student will be able:

1. to apply basic optimization techniques if required during research activities;
2. to understand and implement research results involving more advanced tools of mathematical programming.