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Math. Comput. Appl. 2017, 22(1), 20; doi:10.3390/mca22010020

Global Modulus-Based Synchronous Multisplitting Multi-Parameters TOR Methods for Linear Complementarity Problems

1
School of Science, Zhengzhou University of Aeronautics, Zhengzhou 450015, China
2
Laboratory of Computationary Physics, Institute of Applied Physics and Computational Mathematics, P.O.Box 8009, Beijing 100088, China
*
Author to whom correspondence should be addressed.
Academic Editor: Guohui Zhao
Received: 17 October 2016 / Revised: 25 January 2017 / Accepted: 27 January 2017 / Published: 21 February 2017
(This article belongs to the Special Issue Information and Computational Science)
View Full-Text   |   Download PDF [239 KB, uploaded 21 February 2017]

Abstract

In 2013, Bai and Zhang constructed modulus-based synchronous multisplitting methods for linear complementarity problems and analyzed the corresponding convergence. In 2014, Zhang and Li studied the weaker convergence results based on linear complementarity problems. In 2008, Zhang et al. presented global relaxed non-stationary multisplitting multi-parameter method by introducing some parameters. In this paper, we extend Bai and Zhang’s algorithms and analyze global modulus-based synchronous multisplitting multi-parameters TOR (two parameters overrelaxation) methods. Moverover, the convergence of the corresponding algorithm in this paper are given when the system matrix is an H + -matrix. View Full-Text
Keywords: modulus-based method; linear complementarity problems; successive relaxation method; H+-matrix modulus-based method; linear complementarity problems; successive relaxation method; H+-matrix
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, L.-T.; Gu, T.-X. Global Modulus-Based Synchronous Multisplitting Multi-Parameters TOR Methods for Linear Complementarity Problems. Math. Comput. Appl. 2017, 22, 20.

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