Stochastic Optimization

Catalog description

This course is designed to provide students with the ability to model optimization problems in uncertain settings and develop and analyze the convergence properties of the associated algorithms. It consists of six parts: 1) Review and Overview of models for decision-making under uncertainty; 2) Stochastic programming (Theory); 3) Decomposition Methods; 4) Monte-Carlo Sampling Methods); 5) Robust optimization; 6) Special topics: Risk-averse optimization, stochastic variational inequality problems; and/or distributed stochastic optimization. Apart from students in Industrial and Manufacturing Engineering, this course would be of interest to students from math, engineering, computer science, statistics, machine learning, economics and operations management. Students are required to have some background in optimization and probability theory.