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Research Article | Open Access

Complexity of a predictor-corrector interior-point method using special kernel function for sufficient WLCPs

Zhuoran Gao1,3, Hongjin Wei4, Suobin Zhang5, Xiaoni Chi1,2,3( )
School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China
Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004, China
Center for Applied Mathematics of Guangxi (GUET), Guilin 541004, China
School of Mathematics and Computer Engineering, Guangxi Science and Technology Normal University, Laibin Guangxi 546199, China
Institute of Scientific Research and Development, Guilin University of Electronic Technology, Guilin 541004, China
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Abstract

A predictor-corrector interior-point method (PC IPM) is developed in this paper to address sufficient weighted linear complementarity problems (WLCPs). Specifically, the analysis is conducted by choosing the parameters of the kernel function φ ( t ) = t p + 1 − 1 p + 1 + t 1 − q − 1 q − 1 to p = 1 and q = 3. Each iteration of the algorithm performs two steps: a predictor step followed by a corrector step. Furthermore, with appropriate parameter selections, we prove that the proposed method remains strictly feasible and converges. A polynomial complexity bound is derived, which is comparable to the best-known iteration bound reported in the literature on PC IPMs for solving sufficient WLCPs. Additionally, preliminary numerical experiments indicate that the proposed algorithm performs effectively.

CLC number: 90C33, 90C51

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AIMS Mathematics
Pages 22576-22597

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Cite this article:
Gao Z, Wei H, Zhang S, et al. Complexity of a predictor-corrector interior-point method using special kernel function for sufficient WLCPs. AIMS Mathematics, 2026, 11(7): 22576-22597. https://doi.org/10.3934/math.2026912

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Received: 11 May 2026
Revised: 07 July 2026
Accepted: 16 July 2026
Published: 15 July 2026
©2026 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)