Matière
Mathématiques
Programme
Doctorat
Lieu
Côte-des-Neiges
Mode d'enseignement
Crédits
3
Description
This course will introduce students to the means of hedging risks in large managerial decision problems where distribution assumptions cannot be made.
More specifically, students will become familiar with the main tools that are used in the application of the robust optimization paradigm: convex theory, data-driven uncertainty sets design, adjustable decision rules, tractable reformulation, and decomposition algorithms for problems of infinite size. The course will also cover a set of practical applications where the use of such tools is called-for. Applications will be inspired from a diversified range of fields of practice.
Thèmes couverts
1) Why is there a recent surge of interest in robust optimization?
2) Robust counterpart of Linear Programs
3) Data-driven Uncertainty Set Design
4) Robust Nonlinear Programming
5) Adjustable Robust Linear Programming
6) Value of Flexibility Using Tractable Decision Rules
7) Globalized Robust Counterparts
8) Distributionally Robust Optimization
9) Robust Markov Decision Processes
10) Robust Preference Optimization
11) Pareto Robust Optimization
12) A Survey of Recent Applications