TIEJ6800 COM1: Stochastic Optimization - Models, Algorithms and Applications (JSS30) (3 cr)
Tentative Topics: This course will include two-stage stochastic programming (LP, MILP, NLP, etc.) models, the concept of expected value of perfect information and value of stochastic solutions, L-shaped (Benders decomposition) algorithm for two-stage stochastic programming, integer L-shaped method, formulations of multi-stage stochastic programming models using scenario tree, stochastic dual dynamic programming (nested Benders decomposition), column or nested column generation (Dantzig-Wolfe decomposition) methods for both scenario based and node based formulations, risk-constrained stochastic programming models, chance-constrained models, Conditional Value at Risk based models, single-stage robust optimization models (both continuous and discrete), two-stage robust optimization models, Benders decomposition applied to RO, the concept of budget of uncertainty.
A variety of real-world applications in various areas (e.g., energy/power engineering, transportation, business, economics, etc.) will be given to motivate research and enhance students understanding of the methodologies.
If time allowed, we will try to cover more advanced decomposition algorithm for both SP and RO, and endogenous versus exogenous uncertainty handling.
This course is an introduction to basic models and algorithms of optimization under uncertainty. This course will cover both stochastic programming and robust optimization. Models will include single-, two-, and multi-stage models; pure linear, nonlinear and mixed integer (linear and nonlinear) models. Benefits, strength, necessity and challenges of these models will be discussed versus deterministic optimization.
Solution algorithms such as reformulation and decomposition methods will be introduced since these models are usually large-scale and complex. In addition, applications in various areas (e.g., electrical power and energy systems engineering, transportation, business, economics, etc.) will be given to motivate research and enhance students understanding of the methodologies.
The main learning outcomes are to grasp those knowledge points, be able to identify and formulate the right models in reality, and to solve them using advanced algorithms with the assists of state of the art optimization solvers.
Useful Links: http://www.gams.com
http://en.wikipedia.org/wiki/Comparison of TeX editors
Description of prerequisites
- J. R. Birge and F. Louveaux, Introduction to Stochastic Programming, Springer Verlag, New York, 1997
- Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski, Robust Optimization, Princeton University Press, 2009