Stochastic Nonlinear Complementarity Problems: Stochastic Programming Reformulation and Penalty-Based Approximation Method

被引：14
|
作者：
Wang, M. [1 ,2 ]
Ali, M. M. [2 ]
机构：
[1] Dalian Univ Technol, Sch Management, Dalian 116024, Peoples R China
[2] Univ Witwatersrand, Sch Computat & Appl Math, ZA-2050 Johannesburg, South Africa
来源：
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2010年 / 144卷 / 03期
关键词：
Stochastic nonlinear complementarity problems; Stochastic programming; Sample average approximation; Penalty method; Convergence; MATHEMATICAL PROGRAMS; EQUALITY CONSTRAINTS; EQUILIBRIUM;
D O I：
10.1007/s10957-009-9606-4
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：
We consider a class of stochastic nonlinear complementarity problems. We first reformulate the stochastic complementarity problem as a stochastic programming model. Based on the reformulation, we then propose a penalty-based sample average approximation method and prove its convergence. Finally, we report on some numerical test results to show the efficiency of our method.
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页码：597 / 614
页数：18
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