ISE-6454: Stochastic and Robust Optimization

Description: Introduction to mathematical models, theory and computational methods for optimization problems with uncertain data parameters and discrete/continuous decisions. Mix of theoretical concepts and numerical algorithms to solve stochastic, robust, and distributionally robust programming problems. Modeling for real world problems in domains including homeland security, healthcare, and power systems, using stochastic and robust programming. Theoretical properties and reformulation techniques for stochastic and robust programs. Numerical methods: Benders, regularized, generalized, and nested Benders, Dantzig-Wolfe, L-shaped, integer L-shaped, and statistically motivated decomposition methods. Value of stochastic and robust solutions, and sensitivity analysis using distributionally robust optimization. Convergence analysis of the numerical methods.

Pathways: N/A

Course Hours: 3 credits

Corequisites: N/A

Crosslist: N/A

Repeatability: N/A