A Neural Network Based on the Metric Projector for Solving SOCCVI Problem

Juhe Sun, Weichen Fu, Jan Harold Alcantara, Jein Shan Chen*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

We propose an efficient neural network for solving the second-order cone constrained variational inequality (SOCCVI). The network is constructed using the Karush-Kuhn-Tucker (KKT) conditions of the variational inequality (VI), which is used to recast the SOCCVI as a system of equations by using a smoothing function for the metric projection mapping to deal with the complementarity condition. Aside from standard stability results, we explore second-order sufficient conditions to obtain exponential stability. Especially, we prove the nonsingularity of the Jacobian of the KKT system based on the second-order sufficient condition and constraint nondegeneracy. Finally, we present some numerical experiments, illustrating the efficiency of the neural network in solving SOCCVI problems. Our numerical simulations reveal that, in general, the new neural network is more dominant than all other neural networks in the SOCCVI literature in terms of stability and convergence rates of trajectories to SOCCVI solution.

原文英語
文章編號9159914
頁(從 - 到)2886-2900
頁數15
期刊IEEE Transactions on Neural Networks and Learning Systems
32
發行號7
DOIs
出版狀態已發佈 - 2021 七月

ASJC Scopus subject areas

  • 軟體
  • 電腦科學應用
  • 電腦網路與通信
  • 人工智慧

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