Epi-convergent Scenario Generation Method for Stochastic Problems via Sparse Grid

Michael Chen(michael-chennorthwestern.edu)
Sanjay Mehrotra(mehrotraiems.northwestern.edu)

Abstract: One central problem in solving stochastic programming problems is to generate moderate-sized scenario trees which represent well the risk faced by a decision maker. In this paper we propose an efficient scenario generation method based on sparse grid, and prove it is epi-convergent. Furthermore, we show numerically that the proposed method converges to the true optimal value fast in comparison with Monte Carlo and Quasi Monte Carlo methods.

Keywords: stochastic programming, convex programming, scenario generation, sparse grid

Category 1: Stochastic Programming

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Citation: department of IE/MS Northwestern University 04/2008

Download: [PDF]

Entry Submitted: 03/24/2008
Entry Accepted: 03/24/2008
Entry Last Modified: 03/24/2008

Modify/Update this entry