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has been cited by the following article:

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

  AUTHORS: Qianqian Lu, Chenliang Li

  KEYWORDS: Stochastic Linear Complementarity Problem, Modulus-Based Matrix Splitting, Expected Value Formulation, Positive Semi-Definite Matrix

  JOURNAL NAME: American Journal of Operations Research, Vol.9 No.6, October 24, 2019

  ABSTRACT: 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.