Expected Residual Minimization Method for Stochastic Variational Inequality Problems

被引：0
作者：
M. J. Luo
G. H. Lin
机构：
[1] Dalian University of Technology,Department of Applied Mathematics
来源：
Journal of Optimization Theory and Applications | 2009年 / 140卷
关键词：
Stochastic variational inequalities; Level sets; Quasi-Monte Carlo methods; Convergence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper considers a stochastic variational inequality problem (SVIP). We first formulate SVIP as an optimization problem (ERM problem) that minimizes the expected residual of the so-called regularized gap function. Then, we focus on a SVIP subclass in which the function involved is assumed to be affine. We study the properties of the ERM problem and propose a quasi-Monte Carlo method for solving the problem. Comprehensive convergence analysis is included as well.
引用
收藏
页码：103 / 116
页数：13
相关论文
共 27 条
[1]  
Fukushima M.(1992)Equivalent differentiable optimization problems and descent methods for asymmetric variational inequality problems Math. Program. 53 99-110
[2]  
Chen X.(2005)Expected residual minimization method for stochastic linear complementarity problems Math. Oper. Res. 30 1022-1038
[3]  
Fukushima M.(2009)Robust solution of monotone stochastic linear complementarity problems Math. Program. 117 51-80
[4]  
Chen X.(1997)A stochastic version of a Stackelberg-Nash-Cournot equilibrium model Manag. Sci. 43 190-197
[5]  
Zhang C.(2007)Stochastic SIAM J. Optim. 18 482-506
[6]  
Fukushima M.(1999) matrix linear complementarity problems Math. Program. 84 313-333
[7]  
De Wolf D.(2007)Sample-path solution of stochastic variational inequalities Optimization 56 641-753
[8]  
Smeers Y.(2006)New restricted NCP function and their applications to stochastic NCP and stochastic MPEC Optim. Methods Softw. 21 551-564
[9]  
Fang H.(2008)New reformulations for stochastic nonlinear complementarity problems Oper. Res. Lett. 36 456-460
[10]  
Chen X.(2008)The SC’ property of an expected residual function arising from stochastic complementarity problems J. Optim. Theory Appl. 137 277-295
123