Neural networks for solving second-order cone constrained variational inequality problem

Juhe Sun, Jein Shan Chen*, Chun Hsu Ko

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

38 引文 斯高帕斯(Scopus)

摘要

In this paper, we consider using the neural networks to efficiently solve the second-order cone constrained variational inequality (SOCCVI) problem. More specifically, two kinds of neural networks are proposed to deal with the Karush-Kuhn-Tucker (KKT) conditions of the SOCCVI problem. The first neural network uses the Fischer-Burmeister (FB) function to achieve an unconstrained minimization which is a merit function of the Karush-Kuhn-Tucker equation. We show that the merit function is a Lyapunov function and this neural network is asymptotically stable. The second neural network is introduced for solving a projection formulation whose solutions coincide with the KKT triples of SOCCVI problem. Its Lyapunov stability and global convergence are proved under some conditions. Simulations are provided to show effectiveness of the proposed neural networks.

原文英語
頁(從 - 到)623-648
頁數26
期刊Computational Optimization and Applications
51
發行號2
DOIs
出版狀態已發佈 - 2012 三月

ASJC Scopus subject areas

  • 控制和優化
  • 計算數學
  • 應用數學

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