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Consensus-based Parallel Algorithm for Robust Convex Optimization with Scenario Approach in Colored Network

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

This paper mainly proposes an parallel distributed learning algorithm for the robust convex optimization (RCO). Firstly, the scenario approach is used to transform RCO into its probabilistic approximation Scenario Problem (SP), which is distributively solved by multiprocessors to lighten the computational burden. Secondly, each processor (node) of the colored network processes the local optimization via a primal-dual subgradient algorithm (PDSA) to obtain an optimal solution called a local variable. Finally, a consensus method named the Colored Distributed Average Consensus (CDAC), which is based on Distributed Average Consensus (DAC), is proposed to act on the whole local variables to obtain the global optimal solution. Experimental results show that CDAC has an advantage in terms of computational time over DAC, while they have the same results.

This study was supported by the National Natural Science Foundation of China under Grant no. 61672477.

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Authors and Affiliations

  1. College of Sciences, China Jiliang University, Hangzhou, 310018, Zhejiang Province, China

    Fan Feng & Feilong Cao

Authors
  1. Fan Feng
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  2. Feilong Cao
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Corresponding author

Correspondence to Feilong Cao .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Feng, F., Cao, F. (2017). Consensus-based Parallel Algorithm for Robust Convex Optimization with Scenario Approach in Colored Network. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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