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A One-Step Smoothing Newton Method Based on a New Class of One-Parametric Nonlinear Complementarity Functions for P 0-NCP

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Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

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Abstract

Nonlinear complementarity problem with P 0-function is studied. Based on a new class of one-parametric nonlinear complementarity functions, the problem is approximated by a family of parameterized smooth equations and a one-step smoothing Newton method is presented. The proposed algorithm only need to solve one system of linear equations and perform one line search per iteration. It is proved to be convergent globally and superlinearly without strict complementarity. Moreover, the algorithm has locally quadratic convergence under mild conditions.

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Author information

Authors and Affiliations

  1. College of Mathematics and System Science, Taishan University, 271021, Tai’an, P.R. China

    Liang Fang, Xianming Kong, Xiaoyan Ma & Wei Zhang

  2. Department of Mathematics, Heze University, 274015, Heze, P.R. China

    Han Li

Authors
  1. Liang Fang
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  2. Xianming Kong
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  3. Xiaoyan Ma
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  4. Han Li
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  5. Wei Zhang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Fang, L., Kong, X., Ma, X., Li, H., Zhang, W. (2010). A One-Step Smoothing Newton Method Based on a New Class of One-Parametric Nonlinear Complementarity Functions for P 0-NCP. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-13278-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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