A Branch and Bound Method for Stochastic Global Optimization

Norkin VI, Pflug GC, & Ruszczynski A (1996). A Branch and Bound Method for Stochastic Global Optimization. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-96-065

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Abstract

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.

Item Type: Monograph (IIASA Working Paper)
Research Programs: Optimization under Uncertainty (OPT)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:07
Last Modified: 19 Jul 2016 15:29
URI: http://pure.iiasa.ac.at/4962

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