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A Neural Network for Constrained Saddle Point Problems: An Approximation Approach

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

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Abstract

This paper proposes a neural network for saddle point problems (SPP) by an approximation approach. It first proves both the existence and the convergence property of approximate solutions, and then shows that the proposed network is globally exponentially stable and the solution of (SPP) is approximated. Simulation results are given to demonstrate further the effectiveness of the proposed network.

Supported by the National Key Basic Research Project (973 Project)(2002cb312205), the Grant of the NSF of China(10471114), and the Grant of the NSF of Fujian Province, China (A04100021).

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Author information

Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, China

    Xisheng Shen & Shiji Song

  2. School of Mathematical Sciences, Xiamen University, Fujian, Xiamen, 361005, China

    Xisheng Shen & Lixin Cheng

Authors
  1. Xisheng Shen
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  2. Shiji Song
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  3. Lixin Cheng
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Shen, X., Song, S., Cheng, L. (2005). A Neural Network for Constrained Saddle Point Problems: An Approximation Approach. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_60

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  • DOI: https://doi.org/10.1007/11539087_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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