Skip to main content
SpringerLink
Log in

A Pointer Network Based Deep Learning Algorithm for the Max-Cut Problem

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11301))

Included in the following conference series:

Abstract

The max-cut problem is one of the classic NP-hard combinatorial optimization problems. In order to solve this problem efficiently, the paper mainly studies the topic of using the pointer network to build a training model to solve the max-cut problem. Then, the network model is trained with supervised learning. The experimental results show that the network trained by this algorithm can obtain the approximate solution to the max-cut problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mehlhorn, K.: NP-completeness. Eatcs Monogr. Theor. Comput. Sci. 5(3), 359–376 (1984)

    Google Scholar 

  2. Bie, T.D., Cristianini, N.: Fast SDP relaxations of graph cut clustering, transduction, and other combinatorial problem. J. Mach. Learn. Res. 7(3), 1409–1436 (2006)

    MathSciNet  MATH  Google Scholar 

  3. Croce, F.D., Kaminski, M.J., Paschos, V.T.: An exact algorithm for MAX-CUT in sparse graphs. Oper. Res. Lett. 35(3), 403–408 (2007)

    Article  MathSciNet  Google Scholar 

  4. Krishnan, K., Mitchell, J.E.: A semidefinite programming based polyhedral cut and price approach for the maxcut problem. Comput. Optim. Appl. 33(1), 51–71 (2006)

    Article  MathSciNet  Google Scholar 

  5. Funabiki, N., Kitamichi, J., Nishikawa, S.: An evolutionary neural network algorithm for max cut problems. In: International Conference on Neural Networks, vol. 2, pp. 1260–1265. IEEE (1997)

    Google Scholar 

  6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, vol. 60, pp. 1097–1105. Curran Associates Inc. (2012)

    Google Scholar 

  7. Deng, L., Li, J., Huang, J.T., Yao, K., Yu, D., Seide, F., et al.: Recent advances in deep learning for speech research at Microsoft. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8604–8608. IEEE (2013)

    Google Scholar 

  8. Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: International Conference on Neural Information Processing Systems. MIT Press (2015)

    Google Scholar 

  9. Milan, A., Rezatofighi, S.H., Garg, R., Dick, A., Reid, I.: Data-driven approximations to NP-hard problems (2017)

    Google Scholar 

  10. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks, vol. 4, pp. 3104–3112 (2014)

    Google Scholar 

  11. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comput. Sci. (2014)

    Google Scholar 

  12. Sak, H., Senior, A., Beaufays, F.: Long short-term memory recurrent neural network architectures for large vocabulary speech recognition. Comput. Sci. 338–342 (2014)

    Google Scholar 

  13. Acuna-Agost, R., Acuna-Agost, R.: Deep choice model using pointer networks for airline itinerary prediction. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1575–1583. ACM (2017)

    Google Scholar 

  14. Zhou, M.X.: A benchmark generator for Boolean quadratic programming. Comput. Sci. (2015)

    Google Scholar 

  15. Barahona, F., Junger, M., Reinelt, G.: Experiments in quadratic 0–1 programming. Math. Program. 44(1–3), 127–137 (1989)

    Article  MathSciNet  Google Scholar 

  16. Gu, S., Hao, T.: A pointer network based deep learning algorithm for 0–1 Knapsack Problem. In: International Conference on Advanced Computational Intelligence (ICACI 2018), pp. 357–361 (2018)

    Google Scholar 

  17. Gu, S., Hao, T., Yang, S.: The implementation of a pointer network model for traveling salesman problem on a Xilinx PYNQ board. In: Huang, T., Lv, J., Sun, C., Tuzikov, A.V. (eds.) ISNN 2018. LNCS, vol. 10878, pp. 130–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92537-0_16

    Chapter  Google Scholar 

Download references

Acknowledgments

The work described in the paper was supported by the National Science Foundation of China under Grant 61876105.

Author information

Authors and Affiliations

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

    Shenshen Gu & Yue Yang

Authors
  1. Shenshen Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shenshen Gu .

Editor information

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

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gu, S., Yang, Y. (2018). A Pointer Network Based Deep Learning Algorithm for the Max-Cut Problem. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04167-0_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04166-3

  • Online ISBN: 978-3-030-04167-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation