Decomposition methods in stochastic programming

被引：5
作者：
Andrzej Ruszczyński
机构：
[1] University of Wisconsin-Madison,Department of Industrial Engineering
[2] Warsaw University of Technology,Department of Electronics and Computer Science
来源：
Mathematical Programming | 1997年 / 79卷
关键词：
Stochastic programming; Decomposition; Primal methods; Dual methods; Stochastic methods;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastic programming problems.
引用
收藏
页码：333 / 353
页数：20
相关论文
共 42 条
[11]  
Dantzig G.(1979)-shaped method for stochastic integer programs with complete recourse SIAM Journal on Numerical Analysis 16 964-979
[12]  
Wolfe P.(1996)Splitting algorithms for the sum of two nonlinear operators Mathematical Programming 75 201-220
[13]  
Eckstein J.(1995)Differentiation formulas for probability functions: the transformation method Operations Research 43 477-490
[14]  
Bertsekas D.P.(1996)A new scenario decomposition method for large-scale stochastic optimization Mathematics of Operations Research 21 513-528
[15]  
Glynn P.W.(1976)Analysis of sample path optimization SIAM Journal on Control and Optimization 14 877-898
[16]  
Iglehart D.L.(1976)Monotone operators and the proximal point algorithm Mathematics of Operations Research 1 97-116
[17]  
King A.J.(1991)Augmented Lagrangians and applications of the proximal point algorithm in convex programming Mathematics of Operations Research 16 1-23
[18]  
Rockafellar R.T.(1996)Scenarios and policy aggregation in optimization under uncertainty SIAM Journal on Optimization 6 531-547
[19]  
Laporte G.(1986)Lipschitz stability for stochastic programs with complete recourse Mathematical Programming 35 309-333
[20]  
Louveaux F.V.(1987)A regularized decomposition method for minimizing a sum of polyhedral functions Mathematics of Operations Research 12 32-49
12345