Recurrent neural network approach for partitioning irregular graphs

  • M-Tahar Kechadi
Track C2: Computational Science

DOI: 10.1007/BFb0100606

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

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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Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • M-Tahar Kechadi
    • 1
  1. 1.Parallel Computational Research Group, Department of Computer ScienceUniversity College DublinDublin 4Ireland

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