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A projected-based neural network method for second-order cone programming

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Abstract

A projected-based neural network method for second-order cone programming is proposed. The second-order cone programming is transformed into an equivalent projection equation. The projection on the second-order cone is simple and costs less computation time. We prove that the proposed neural network is stable in the sense of Lyapunov and converges to an exact solution of the second-order cone programming problem. The simulation experiments show our method is an efficient method for second-order cone programming problems.

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Authors and Affiliations

  1. School of Computer Science, Xi’an Science and Technology University, Xi’an, 710054, China

    Yaling Zhang

  2. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    Yaling Zhang

Authors
  1. Yaling Zhang
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Correspondence to Yaling Zhang.

Additional information

This work was supported by the National Science Foundations for Young Scientists of China (11101320, 61201297), National Science Basic Research Plan in ShaanXi Province of China (Program No. 2015JM1031), and the Fundamental Research Funds for the Central Universities (JB150713).

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Zhang, Y. A projected-based neural network method for second-order cone programming. Int. J. Mach. Learn. & Cyber. 8, 1907–1914 (2017). https://doi.org/10.1007/s13042-016-0569-0

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  • DOI: https://doi.org/10.1007/s13042-016-0569-0

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