@article{Gao2026, 
author = {Zhuoran Gao and Hongjin Wei and Suobin Zhang and Xiaoni Chi},
title = {Complexity of a predictor-corrector interior-point method using special kernel function for sufficient WLCPs},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {7},
pages = {22576-22597},
keywords = {predictor-corrector interior-point method, sufficient weighted linear complementarity problem, full-Newton step, proximity measure, kernel function},
url = {https://www.sciopen.com/article/10.3934/math.2026912},
doi = {10.3934/math.2026912},
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.}
}