A parallel improvement algorithm for the bipartite subgraph problem

  • Authors:
  • K. C. Lee;N. Funabiki;Y. Takefuji

  • Affiliations:
  • Cirrus Logic Inc., Fremont, CA;-;-

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 1992

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Abstract

The authors propose the first parallel improvement algorithm using the maximum neural network model for the bipartite subgraph problem. The goal of this NP-complete problem is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that the algorithm finds a solution within 200 iteration steps and the solution quality is superior to that of the best existing algorithm. The algorithm is extended for the K-partite subgraph problem where no algorithm has been proposed