Modulus-Based Matrix Splitting Iteration Methods for a Class of Stochastic Linear Complementarity Problem

• 作者: Chenliang Li Qianqian Lu
• 来源期刊: 美国运筹学期刊(英文)
• 学科: 数学
• 关键词: Stochastic Linear Complementarity Problem Modulus-Based MATRIX Splitting EXPECTED Value Formulation Positive Semi-Definite MATRIX
• 年，卷（期）: 2019,（6）
• 页码范围: 245-254
• 页数: 10页
• 分类号: O17

摘要

For the expected value formulation of stochastic linear complementarity problem, we establish modulus-based matrix splitting iteration methods. The convergence of the new methods is discussed when the coefficient matrix is a positive definite matrix or a positive semi-definite matrix, respectively. The advantages of the new methods are that they can solve the large scale stochastic linear complementarity problem, and spend less computational time. Numerical results show that the new methods are efficient and suitable for solving the large scale problems.