MA 662 Stochastic Programming

This course introduces students to modeling and numerical techniques for optimization under uncertainty and risk. Topics include: generalized concavity of measures, optimization problems with probabilistic constraints (convexity, differentiability, optimality, and duality), numerical methods for solving problems with probabilistic constraints, two-stage and multi-stage models (structure, optimality, duality), decomposition methods for two-stage and multi-stage models, risk averse optimization models

Credits

3

Prerequisite

MA 540 and MA 547

Distribution

Pure and Applied Mathematics Program

Offered

Fall Semester Spring Semester