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A branch and bound method for stochastic global optimization 总被引:9,自引:0,他引:9
Vladimir I. Norkin Georg Ch. Pflug Andrzej Ruszczyński 《Mathematical Programming》1998,83(1-3):425-450
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. 相似文献