Stochastic Optimization Lecture

General Information:

Detailed course description

Content:

The course gives an introduction to the topic of stochastic optimization. Students will learn about the underlying concepts and the theory of stochastic optimization as well as algorithmic solution techniques. The theory will be complemented by numerous illustrative classical stochastic optimization examples such as the newsvendor problem and examples from the eld of energy markets. The lectures are complemented by a lab, which gives students the possibility to deepen their understanding of the theory and try out algorithms and techniques presented in the lecture.

The topics that will be covered are: ˆ

• Introduction & Basic Modelling ˆ
• Two-stage Linear Stochastic Optimization Models and their deterministic equivalents ˆ
• The L-Shaped Method for Two-Stage Stochastic Optimization ˆ
• Monte-Carlo Methods ˆ
• Multi-Stage Stochastic Optimization ˆ
• Risk Measures in Stochastic Optimization

 

Recommended Reading:

• Birge, J. and Louveaux, F. Introduction to Stochastic Programming. Springer Series in Operations Research and Financial Engineering, 2011 (second edition).
• ˆShapiro, A. and Dentcheva, D. and Ruszczynski, A. Lectures on Stochastic Programming: Modeling and Theory. MOS-SIAM Series on Optimization. 2014 (second edition). http://www2.isye.gatech.edu/people/faculty/Alex_Shapiro/SPbook.pdf