A smooth penalty-based sample average approximation method for stochastic complementarity problems

被引：3

作者：

He, Suxiang ^{[1
]}

Wei, Min ^{[1
]}

Tong, Hengqing ^{[1
]}

机构：

[1] Wuhan Univ Technol, Sch Sci, Wuhan 430070, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2015年 / 287卷

基金：

中国国家自然科学基金;

关键词：

Stochastic complementarity problems; Sample average approximation method; Penalty function; Stationary point; Convergence; VARIATIONAL INEQUALITY PROBLEMS; MATHEMATICAL PROGRAMS; EQUALITY CONSTRAINTS; EQUILIBRIUM;

D O I：

10.1016/j.cam.2015.03.017

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Sample average approximation method is one of the effective methods in the stochastic optimization. A smooth penalty-based sample average approximation method for stochastic nonlinear complementarity problems is presented in this paper. Based on a smooth penalty function, a reformulation is proposed for the equivalent problem of EV formulation for stochastic complementary problems and it is proven that its solutions are existent under some mild assumptions. An implementable sample average approximation method for the reformulation is further established and its convergence is analyzed. The numerical results for some test examples are reported at last to show efficiency of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：20 / 31

页数：12

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