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

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/4308 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/4308 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/4308 archive 351 disk0/00/00/50/05 2016-01-15 02:08:14 2021-08-27 17:15:51 2016-01-15 02:08:14 monograph show 3 Pflug G.C. 1361 0000-0001-8215-3550 Ruszczynski A. 1475 Schultz R. On the Glivenko-Cantelli Problem in Stochastic Programming: Linear Recourse and Extensions pub iis_met prog_opt 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. 1996-02 published WP-96-020 WP-96-020 10 229 1544 public working_paper IIASA, Laxenburg, Austria 24 FALSE info:eu-repo/semantics/book