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Stochastic Approximation Methods for the Two-stage Stochastic Linear Complementarity Problem

Abstract 

In this paper, aiming to solve the TSLCP of large-scale, we propose two kinds of stochastic methods, namely the stochastic approximation  method based on the projection (SAP) and the dynamic sampling stochastic approximation  method based on the projection (DS-SAP), both of which offer inexpensive computational costs in solving subproblems, especially compared with PHA. In particular, the linear complementarity subproblems are solved inexactly during each iteration, and the convergence analysis of both SAP and DS-SAP with inexactness criterion is rigorously presented. Moreover, numerical implementations and practical applications demonstrate the efficiency of our proposed methods. 


Short bio 

陈林,男,现任重庆师范大学陈林讲师。1987 年 8 月出生于四川,讲师,四川大学博士,电子科技大学博士后。 

近年来主持或参加的主要科研项目包括: 
(1) 国家自然科学基金委员会, 重大项目, 11991023, 连续优化的自主学习方法; 
(2) 国家自然科学基金委员会, 面上项目, 11771064, 基于标量化方法的向量优化问题近似解的研究。