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A Globally Convergent Smoothing Method for Symmetric Conic Linear Programming

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Advances in Computation and Intelligence (ISICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5821))

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Abstract

Based on the Chen-Harker-Kanzow-Smale smoothing function, a smoothing Newton method is developed for solving the symmetric conic linear programming. Without any restrictions for its starting point, this algorithm solves only one linear system of equations at each iteration and proves to be globally convergent in absence of uniform nonsingularity. Numerical results indicate that it is promising in future applications.

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Author information

Authors and Affiliations

  1. College of Mathematics and Information Science, Huanggang Normal University, Huangzhou, 438000, Hubei, China

    Xiaoni Chi & Ping Li

Authors
  1. Xiaoni Chi
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  2. Ping Li
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  2. School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, China

    Zhenhua Li

  3. Computation Center, Wuhan University, 430072, Wuhan, Hubei, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, 965-8580, i, Aizu-Wakamatsu City, Fukushima, Japan

    Yong Liu

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© 2009 Springer-Verlag Berlin Heidelberg

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Chi, X., Li, P. (2009). A Globally Convergent Smoothing Method for Symmetric Conic Linear Programming. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_15

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  • DOI: https://doi.org/10.1007/978-3-642-04843-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04842-5

  • Online ISBN: 978-3-642-04843-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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