A branch and bound method for stochastic global optimization

Pflug GC, Norkin VI, & Ruszczynski A (1998). A branch and bound method for stochastic global optimization. Mathematical Programming: 425-450. DOI:10.1007/BF02680569.

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Abstract

A stochastic branch and bound method for solving stochastic global optimization problems is proposed. As in the deterministic case, the feasible set is partitioned into compact subsets. To guide the partitioning process the method uses stochastic upper and lower estimates of the optimal value of the objective function in each subset. Convergence of the method is proved and random accuracy estimates derived. Methods for constructing stochastic upper and lower bounds are discussed. The theoretical considerations are illustrated with an example of a facility location problem.

Item Type: Article
Research Programs: Risk, Modeling, Policy (RMP)
Bibliographic Reference: Mathematical Programming; 83:425-450 [1998]
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:09
Last Modified: 21 Dec 2016 10:44
URI: http://pure.iiasa.ac.at/5410

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