5 credits
30.0 h + 22.5 h
Q2
Teacher(s)
Papavasiliou Anthony;
Language
English
Prerequisites
- Fluency in English at the level of course LANGL1330
- Linear programming
- Familiarity with probability theory
- Familiarity with math programming languages (Matlab, AMPL)
Main themes
- Optimization algorithms: dynamic programming, cutting plane methods, decomposition algorithms
- Mathematical programming models and languages
- Applications: finance, logistics, energy
Aims
At the end of this learning unit, the student is able to : | |
| 1 | In reference to the AA standard, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
At the end of the course, students will be able to:
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Mathematical background (duality, probability theory)
- Stochastic programming models
- Value of perfect information and the value of the stochastic solution
- Cutting plane algorithms
- Dynamic programming
- Stochastic dual dynamic programming
- Lagrange relaxation
Teaching methods
2 hours of magistral courses per week, and 2 hours of training sessions per week. Homeworks will be evaluated by the instructor and/or the teaching assistant.
Evaluation methods
- Written exam
- Regular homework assignments
Online resources
Bibliography
- Notes de cours
- Impressions de manuels ou articles fournies au cours. Le livre suivant servira de support pour la plupart du cours : John Birge, Francois Louveaux, "Introduction to Stochastic Programming"
Faculty or entity
MAP
