**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