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SPEPS Preprint

Author(s): Alexander Shapiro, Georgia Institute of Technology
Joocheol Kim, Georgia Institute of Technology
Tito Homem-de-Mello, The Ohio State University
Title: Conditioning of stochastic programs
Date of Acceptance: 26.06.2000
Submission Date: 24.05.2000
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint: pdf (urn:nbn:de:kobv:11-10057688)
Keywords (eng): Monte Carlo simulation, stochastic programming, Large Deviations theory, ill conditioned problems
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Abstract (eng):
In this paper we consider stochastic programming problems where the objective function is given as an expected value function. With an optimal solution of such a (convex) problem we associate a condition number which characterizes well or ill conditioning of the problem. We show that the sample size needed to calculate the optimal solution of such problem with a given probability is approximately proportional to the condition number.
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