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Neural Computing and Applications

Erschienen in:

01.09.2014 | Original Article

Weighted max-norm estimate of two-stage splitting method for solving a class of nonlinear complementarity problems

verfasst von: Haijian Yang, Qingguo Li, Jingang Liu

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2014

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Abstract

In this paper, we consider the splitting method and the two-stage splitting method for solving a class of nonlinear complementarity problems with the coefficient matrix being an \(H\)-matrix. Convergence result for the splitting method is presented when the splitting is \(H\)-splitting. Moreover, for the two-stage splitting method, we estimate weighted max-norm bounds for iteration errors, and thereby, we show that the sequence generated by the two-stage iteration scheme converges to the unique solution of the nonlinear complementarity problem without any restriction on the initial vector. Numerical results show that both methods are efficient for solving the class of nonlinear complementarity problems.

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Titel: Weighted max-norm estimate of two-stage splitting method for solving a class of nonlinear complementarity problems
verfasst von: Haijian Yang
Qingguo Li
Jingang Liu
Publikationsdatum: 01.09.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1580-6

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