Courses
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Descriptions
IEMS 451: Stochastic Optimization

Quarter Offered

None ; Morton

Description

Course in stochastic optimization with an emphasis on formulating, solving, and approximating optimization models under uncertainty. Topics include:

  • Models and applications: extensions of the linear programming model; chance-constrained models; stochastic programs with recourse
  • Exact optimization algorithms: extensive forms; L-shaped decomposition and enhancements; extensions to the multistage setting
  • Deterministic approximations and bounds: Jensen and Edmundson-Madansky inequalities; generalized moment problems; sequential approximation algorithms
  • Monte Carlo sampling-based approximations: internal and external approximations; consistency; rates of convergence; solution validation; sequential issues

MATERIALS:

Recommended Text: Lectures on Stochastic Programming: Modeling and Theory, A. Shapiro, D. Dentcheva, and A. Ruszczynksi, Second Edition, MPS/SIAM, 2014 Recommended Software: AMPL: A Modeling Language for Mathematical Programming, R. Fourer, D.M. Gay, and B.W. Kernighan, Second Edition, Duxbury/Thomson, 2003