Optimization under Uncertainty

The objective of this course is to present different techniques to handle uncertainty in decision problems. These techniques will be illustrated on several applications e.g. inventory control, scheduling, energy, machine learning.

Prerequisites: Basic courses in probability and linear programming

Exams: 2 written exams (50%+50%)

Syllabus : Introduction to uncertainty in optimization problems; Reminders (probability, dynamic programming, ...); Markov chains; Markov decision processes; Stochastic programming; Robust optimization

Coordinator: Jean-Philippe Gayon (Jean-Philippe.gayon@grenoble-inp.fr )

Lecturers: Marie-Laure Espinouse; Jean-Philippe Gayon; Jérôme Malick; Gautier Stauffer.

Last modified on August 31, 2016, at 02:01 PM