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A class of new large-update primal-dual interior-point algorithms for P *(κ) nonlinear complementarity problems

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Abstract

In this paper we propose a class of new large-update primal-dual interior-point algorithms for P *(κ) nonlinear complementarity problem (NCP), which are based on a class of kernel functions investigated by Bai et al. in their recent work for linear optimization (LO). The arguments for the algorithms are followed as Peng et al.’s for P *(κ) complementarity problem based on the self-regular functions [Peng, J., Roos, C., Terlaky, T.: Self-Regularity: A New Paradigm for Primal-Dual Interior-Point Algorithms, Princeton University Press, Princeton, 2002]. It is worth mentioning that since this class of kernel functions includes a class of non-self-regular functions as special case, so our algorithms are different from Peng et al.’s and the corresponding analysis is simpler than theirs. The ultimate goal of the paper is to show that the algorithms based on these functions have favorable polynomial complexity.

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Authors and Affiliations

  1. College of Science, China Three Gorges University, Yichang, 443002, P. R. China

    Hua Ping Chen & Ming Wang Zhang

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  1. Hua Ping Chen
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  2. Ming Wang Zhang
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Correspondence to Ming Wang Zhang.

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Supported by Natural Science Foundation of Hubei Province (Grant No. 2008CDZ047)

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Chen, H.P., Zhang, M.W. A class of new large-update primal-dual interior-point algorithms for P *(κ) nonlinear complementarity problems. Acta. Math. Sin.-English Ser. 27, 1979–1994 (2011). https://doi.org/10.1007/s10114-011-9302-9

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  • DOI: https://doi.org/10.1007/s10114-011-9302-9

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