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Recurrent neural network approach for partitioning irregular graphs

  • Track C2: Computational Science
  • Conference paper
  • First Online:
High-Performance Computing and Networking (HPCN-Europe 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1593))

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Abstract

This paper is concerned with utilizing a neural network approach to solve the k-way partitioning problem. The k-way partitioning is modeled as a constraint satisfaction problem with linear inequalities and binary variables. A new recurrent neural network architecture is proposed for k-way partitioning. This network is based on an energy function that controls the competition between the partition's external cost and the penalty function. This method is implemented and compared to other global search techniques such as simulated annealing and genetic algorithms. It is shown that it converges better than these techniques.

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Authors and Affiliations

  1. Parallel Computational Research Group, Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland

    M-Tahar Kechadi

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  1. M-Tahar Kechadi
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Editor information

Peter Sloot Marian Bubak Alfons Hoekstra Bob Hertzberger

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© 1999 Springer-Verlag

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Kechadi, MT. (1999). Recurrent neural network approach for partitioning irregular graphs. In: Sloot, P., Bubak, M., Hoekstra, A., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1999. Lecture Notes in Computer Science, vol 1593. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100606

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  • DOI: https://doi.org/10.1007/BFb0100606

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65821-4

  • Online ISBN: 978-3-540-48933-7

  • eBook Packages: Springer Book Archive

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