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.
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。
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