A branch and bound method for stochastic global optimization

被引：3

作者：

Vladimir I. Norkin

Georg Ch. Pflug

Andrzej Ruszczyński

机构：

[1] International Institute for Applied Systems Analysis,Department of Management Science and Information Systems

[2] Rutgers University,undefined

来源：

Mathematical Programming | 1998年 / 83卷

关键词：

Stochastic programming; Global optimization; Branch and bound method; Facility location;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：425 / 450

页数：25

共 6 条

[1]

Ermoliev Yu.M.(1983)Stochastic quasi-gradient methods and their application to systems optimization Stochastics 4 1-37

[2]

Birge J.R.(1985)Decomposition and partitioning methods for multistage stochastic linear programs Operations Research 33 989-1007

[3]

Mulvey J.M.(1995)A new scenario decomposition method for large-scale stochastic optimization Operations Research 43 477-490

[4]

Ruszczyński A.(1991)Scenarios and policy aggregation in optimization under uncertainty Mathematics of Operations Research 16 1-23

[5]

Rockafellar R.T.(undefined)undefined undefined undefined undefined-undefined

[6]

Wets R.J.-B.(undefined)undefined undefined undefined undefined-undefined

← 1 →