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Research Article | Open Access

A predictor-corrector interior-point algorithm for P ∗ ( κ )-weighted linear complementarity problems

Lu Zhang1, Xiaoni Chi1( ), Suobin Zhang2, Yuping Yang3,4
School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004, China
Institute of Scientific Research and Development, Guilin University of Electronic Technology, Guilin 541004, China
School of Mathematics and Computing Science, Guangxi Key Laboratory of Automatic Detection Technology and Instruments, Guilin University of Electronic Technology, Guilin 541004, China
Center for Applied Mathematics of Guangxi (GUET), Guilin 541004, China
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Abstract

In this paper, we present a predictor-corrector interior-point algorithm for P ∗ ( κ )-weighted linear complementarity problems. Based on the kernel function φ ( t ) = t , the search direction of the algorithm is obtained. By choosing appropriate parameters, we prove that the algorithm is feasible and convergent. It is shown that the proposed algorithm has polynomial iteration complexity. Numerical results illustrate the effectiveness of the algorithm.

Keywords

predictor-corrector interior-point algorithmP∗(κ)-weighted linear complementarity problemsearch directionproximity measurepolynomial complexity
CLC number: 90C33, 90C51

References

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AIMS Mathematics
Volume 8 Issue 4,
April 2023
Pages 9212-9229
DOI: 10.3934/math.2023462

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Cite this article:
Zhang L, Chi X, Zhang S, et al. A predictor-corrector interior-point algorithm for P ∗ ( κ )-weighted linear complementarity problems. AIMS Mathematics, 2023, 8(4): 9212-9229. https://doi.org/10.3934/math.2023462

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Received: 25 September 2022
Revised: 18 January 2023
Accepted: 01 February 2023
Published: 15 April 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)