数学与系统科学研究院

计算数学所学术报告

报告人：        Masao Fukushima

Kyoto University, Japan

报告题目 A New Formulation for Stochastic Linear Complementarity Problems

Abstract: 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 $\omega^i$ such that the coefficient matrix $M(\omega^i)$ is an $R_0$ 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.

报告时间：2004年8月3日  下午3：00-4：30

报告地点：科技综合楼三层报告厅