American Journal of Operations Research

Volume 9, Issue 6 (November 2019)

ISSN Print: 2160-8830   ISSN Online: 2160-8849

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

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DOI: 10.4236/ajor.2019.96016    216 Downloads   386 Views   Citations

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.

Cite this paper

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.

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