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A Large-update Interior-point Algorithm for P∗(κ) Linear Complementarity Problem Based on a New Class of Hyperbolic Kernel Functions

Le Ma & Mingwang Zhang

Asia-Pacific Journal of Operational Research2026https://doi.org/10.1142/s0217595926500053article
AJG 1
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0.50

What the paper says

In this paper, we present and analyze a large-update interior-point algorithm for [Formula: see text] linear complementarity problem. The search directions are introduced based on a new class of hyperbolic kernel functions. To the best of our knowledge, this class of kernel functions differs from all the existing hyperbolic kernel functions in the literature by having a growth term that is between linear and quadratic. By establishing some technical lemmas, we show that the algorithm has favorable complexity results. Some numerical results illustrate the efficiency of the algorithm.

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https://doi.org/https://doi.org/10.1142/s0217595926500053

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@article{le2026,
  title        = {{A Large-update Interior-point Algorithm for P∗(κ) Linear Complementarity Problem Based on a New Class of Hyperbolic Kernel Functions}},
  author       = {Le Ma & Mingwang Zhang},
  journal      = {Asia-Pacific Journal of Operational Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0217595926500053},
}

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A Large-update Interior-point Algorithm for P∗(κ) Linear Complementarity Problem Based on a New Class of Hyperbolic Kernel Functions

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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