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<p>A family of neural networks is proposed to solve linear complementarity problems (LCP). The neural networks are constructed from the novel equivalent model of LCP, which is reformulated by utilizing the modulus and smoothing technologies. Some important properties of the proposed novel equivalent model are summarized. In addition, the stability properties of the proposed steepest descent-based neural networks for LCP are analyzed. In order to illustrate the theoretical results, we provide some numerical simulations and compare the proposed neural networks with existing neural networks based on the NCP-functions. Numerical results indicate that the performance of the proposed neural networks is effective and robust.</p>

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A neural network based on novel equivalent model for linear complementarity problems

  • 期刊论文

  • 发表日期：

    2025年6月26日

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  • Yi-fen Ke,
  • Ya-jun Xie,
  • Huai Zhang,
  • Chang-feng Ma

摘要

A family of neural networks is proposed to solve linear complementarity problems (LCP). The neural networks are constructed from the novel equivalent model of LCP, which is reformulated by utilizing the modulus and smoothing technologies. Some important properties of the proposed novel equivalent model are summarized. In addition, the stability properties of the proposed steepest descent-based neural networks for LCP are analyzed. In order to illustrate the theoretical results, we provide some numerical simulations and compare the proposed neural networks with existing neural networks based on the NCP-functions. Numerical results indicate that the performance of the proposed neural networks is effective and robust.