A Branch and Bound Method for Stochastic Global Optimization
AbstractA 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.
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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number wp96065.
Date of creation: Jun 1996
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