Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/4305 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/4305 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/4305 archive 351 disk0/00/00/50/02 2016-01-15 02:08:11 2021-08-27 17:15:51 2016-01-15 02:08:11 monograph show 2 Futschik A. Pflug G.C. 1361 0000-0001-8215-3550 Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization pub iis_met prog_opt Approximate solutions for discrete stochastic optimization problems are often obtained via simulation. It is reasonable to complement these solutions by confidence regions for the argmin-set. We address the question, how a certain total number of random draws should be distributed among the set of alternatives. We propose a one-step allocation rule which turns out to be asymptotically optimal in the case of normal errors for two goals: To minimize the costs caused by using only an approximate solution and to minimize the expected size of the confidence sets. 1996-03 published WP-96-023 WP-96-023 10 229 public working_paper IIASA, Laxenburg, Austria 18 FALSE info:eu-repo/semantics/book