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A Neural Network Based Global Optimal Algorithm for Unconstrained Binary Quadratic Programming Problem

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Book cover Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11302))

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Abstract

Unconstrained binary quadratic programming problem is a classical integer optimization problem and is well known to be NP-hard. In order to improve the performance of global optimal algorithms for unconstrained binary quadratic programming problem, in this paper, we proposed a new exact solution method. By investigating the geometric properties of the original problem, the quality of the algorithms for calculating the upper bound and lower bound are improved. And then, for the new derived upper bound algorithm and lower bound algorithm, their recurrent neural network models are proposed and applied respectively in order to speed up the computation. The numerical results shows that the proposed algorithm of a branch-and-bound type is quite effective and efficient.

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Acknowledgments

The work described in the paper was supported by the National Science Foundation of China under Grant 61503233.

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

    Shenshen Gu & Xinyi Chen

Authors
  1. Shenshen Gu
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  2. Xinyi Chen
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Corresponding author

Correspondence to Shenshen Gu .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Gu, S., Chen, X. (2018). A Neural Network Based Global Optimal Algorithm for Unconstrained Binary Quadratic Programming Problem. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_51

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  • DOI: https://doi.org/10.1007/978-3-030-04179-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04178-6

  • Online ISBN: 978-3-030-04179-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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