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Improving the Efficiency of Dynamic Programming in Big Data Computing

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10656))

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Abstract

In this paper we present the extremely simple algorithms to improve the performance of dynamic programming, one of the fundamental techniques for solving optimization problems, in the environment of data-intensive computing. These algorithms are applied to several NP hard combinatorial optimization problems. The presented algorithms decrease the time and space complexity of dynamic programming algorithms by exploiting word parallelism. The computational experiments demonstrate that the achieved results are not only of theoretical interest, but also that the techniques developed may actually lead to considerably faster algorithms.

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Acknowledgment

This work was supported in part by the Quanzhou Foundation of Science and Technology under Grant No. 2013Z38, Fujian Provincial Key Laboratory of Data-Intensive Computing and Fujian University Laboratory of Intelligent Computing and Information Processing.

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Authors and Affiliations

  1. Fujian University of Technology, Fuzhou, 350108, China

    Xiaodong Wang

  2. Quanzhou Normal University, Quanzhou, 362000, China

    Daxin Zhu

Authors
  1. Xiaodong Wang
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  2. Daxin Zhu
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Corresponding author

Correspondence to Daxin Zhu .

Editor information

Editors and Affiliations

  1. Guangzhou University , Guangzhou, China

    Guojun Wang

  2. Edith Kinney Gaylord Presidential Professor, University of Oklahoma, Norman, Oklahoma, USA

    Mohammed Atiquzzaman

  3. Aalto University, Espoo, Finland

    Zheng Yan

  4. University of Texas at San Antonio, San Antonio, Texas, USA

    Kim-Kwang Raymond Choo

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Wang, X., Zhu, D. (2017). Improving the Efficiency of Dynamic Programming in Big Data Computing. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-72389-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72388-4

  • Online ISBN: 978-3-319-72389-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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