Mingwang Zhang, Jiawei Zhong, Shan Wu, Dechun Zhu, A full-Newton step feasible interior-point algorithm based on a simple kernel function for $latex P_*(\kappa)$-horizontal linear complementarity problem, Vol. 2023 (2023) Article ID 1, pp. 1-13

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DOI: 10.23952/jnfa.2023.1

Received June 28, 2022; Accepted December 8, 2022; Published January 13, 2023

Abstract In this paper, we propose and analyze an interior-point algorithm with full-Newton steps for $P_*(\kappa)$ -horizontal linear complementarity problem based on a kernel function which was first used to design the interior-point algorithm for linear optimization by Zhang et al. (J Nonlinear Funct. Anal. Article ID 31, 2021). The method mainly based on exploiting the search direction by the kernel function. By using the properties of the kernel function, we prove that the complexity of the algorithm coincides with the currently best known iteration complexity of feasible interior-point methods for $P_*(\kappa)$ -horizontal linear complementarity problem. Finally, some computational results are demonstrated, which show that our algorithm is efficient and promising.

How to Cite this Article:
M. Zhang, J. Zhong, S. Wu, D. Zhu, A full-Newton step feasible interior-point algorithm based on a simple kernel function for $P_*(\kappa)$ -horizontal linear complementarity problem, J. Nonlinear Funct. Anal. 2023 (2023) 1.