Recurrent neural networks for solving second-order cone programs

Chun Hsu Ko, Jein Shan Chen*, Ching Yu Yang

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

29 引文 斯高帕斯(Scopus)

摘要

This paper proposes using the neural networks to efficiently solve the second-order cone programs (SOCP). To establish the neural networks, the SOCP is first reformulated as a second-order cone complementarity problem (SOCCP) with the Karush-Kuhn-Tucker conditions of the SOCP. The SOCCP functions, which transform the SOCCP into a set of nonlinear equations, are then utilized to design the neural networks. We propose two kinds of neural networks with the different SOCCP functions. The first neural network uses the Fischer-Burmeister function to achieve an unconstrained minimization with a merit function. We show that the merit function is a Lyapunov function and this neural network is asymptotically stable. The second neural network utilizes the natural residual function with the cone projection function to achieve low computation complexity. It is shown to be Lyapunov stable and converges globally to an optimal solution under some condition. The SOCP simulation results demonstrate the effectiveness of the proposed neural networks.

原文英語
頁(從 - 到)3646-3653
頁數8
期刊Neurocomputing
74
發行號17
DOIs
出版狀態已發佈 - 2011 10月

ASJC Scopus subject areas

  • 電腦科學應用
  • 認知神經科學
  • 人工智慧

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