Expected residual minimization method for stochastic linear complementarity problems

• 成果类型: Article
• 署名作者: Chen, XJ; Fukushima, M
• 署名单位: Hirosaki University; Kyoto University
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.1050.0160
• 发表日期: 2005
• 页码: 1022-1038
• 关键词: variational-inequalities; ncp-functions

摘要

This paper presents a new formulation for the stochastic linear complementarity problem (SLCP), which aims at minimizing an expected residual defined by an NCP function. We generate observations by the quasi-Monte Carlo methods and prove that every accumulation point of minimizers of discrete approximation problems is a minimum expected residual solution of the SLCP. We show that a sufficient condition for the existence of a solution to the expected residual minimization (ERM) problem and its discrete approximations is that there is an observation omega(1) such that the coefficient matrix M(omega(1)) is an R, matrix. Furthermore, we show that, for a class of problems with fixed coefficient matrices, the ERM problem becomes continuously differentiable and can be solved without using discrete approximation. Preliminary numerical results on a refinery production problem indicate that a solution of the new formulation is desirable.