A Branch and Bound Method for Stochastic Global Optimization

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/4260 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/4260 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/4260 archive 351 disk0/00/00/49/62 2016-01-15 02:07:54 2021-08-27 17:15:46 2016-01-15 02:07:54 monograph show 3 Norkin V.I. AL1055 Pflug G.C. 1361 0000-0001-8215-3550 Ruszczynski A. 1475 A Branch and Bound Method for Stochastic Global Optimization pub iis_met prog_opt A stochastic version of the branch and bound method is proposed for solving stochastic global optimization problems. The method, instead of deterministic bounds, uses stochastic upper and lower estimates of the optimal value of subproblems, to guide the partitioning process. Almost sure convergence of the method is proved and random accuracy estimates derived. Methods for constructing random bounds for stochastic global optimization problems are discussed. The theoretical considerations are illustrated with an example of a facility location problem. 1996-06 published WP-96-065 WP-96-065 10 2245 229 1544 public working_paper IIASA, Laxenburg, Austria 29 FALSE info:eu-repo/semantics/book