On augmented Lagrangian decomposition methods for multistage stochastic programs

被引：22

作者：

Rosa, CH ^{[1
]}

Ruszczynski, A ^{[1
]}

机构：

[1] INT INST APPL SYST ANAL,A-2361 LAXENBURG,AUSTRIA

来源：

ANNALS OF OPERATIONS RESEARCH | 1996年 / 64卷

关键词：

stochastic programming; decomposition; augmented Lagrangian; Jacobi method; parallel computation;

D O I：

10.1007/BF02187650

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios and by decomposing it into nodes corresponding to stages. Theoretical convergence properties of the two approaches are derived and a computational illustration is presented.

引用

页码：289 / 309

页数：21

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