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

Pflug, G. ORCID: https://orcid.org/0000-0001-8215-3550, Ruszczynski, A., & Schultz, R. (1998). On the Glivenko-Cantelli Problem in Stochastic Programming: Linear Recourse and Extensions. Mathematics of Operations Research 23 (1) 204-220. 10.1287/moor.23.1.204.

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Abstract

Integrals of optimal values of random optimization problems depending on a finite dimensional parameter are approximated by using empirical distributions instead of the original measure. Under fairly broad conditions, it is proved that uniform convergence of empirical approximations of the right hand sides of the constraints implies uniform convergence of the optimal values in the linear and convex case.

Item Type:	Article
Research Programs:	Optimization under Uncertainty (OPT)
Depositing User:	Romeo Molina
Date Deposited:	21 Dec 2016 10:27
Last Modified:	27 Aug 2021 17:28
URI:	https://pure.iiasa.ac.at/14189

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