On the Glivenko-Cantelli Problem in Stochastic Programming: Linear Recourse

Authors:   Pflug GC, Ruszczynski A, Schultz R

Publication Year:   1995

Reference:  IIASA Working Paper WP-95-003

Abstract

Integrals of optimal values of random linear programming problems depending on a finite dimensional parameter are approximated by using empirical distributions instead of the original measure. Uniform convergence of the approximations is proved under fairly broad conditions allowing non-convex or discontinuous dependence on the parameter value and random size of the linear programming problem.
KEYWORDS: stochastic programming, empirical measures, uniform convergence

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