Author(s)

Qianqian Lu, Chenliang Li

School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, China.

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.

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

Lu, Q. and Li, C. (2019) Modulus-Based Matrix Splitting Iteration Methods for a Class of Stochastic Linear Complementarity Problem. American Journal of Operations Research, 9, 245-254. doi: 10.4236/ajor.2019.96016.