A parallel improvement algorithm for the bipartite subgraph problem

Kuo Chun Lee, Nobuo Funabiki, Yoshiyasu Takefuji

Research output: Contribution to journalArticle

50 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)139-145
Number of pages7
JournalIEEE Transactions on Neural Networks
Volume3
Issue number1
DOIs
Publication statusPublished - 1992 Jan
Externally publishedYes

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Subgraph
Graph in graph theory
Neural Network Model
Bipartite Graph
Computational complexity
NP-complete problem
Verify
Neural networks
Iteration
Simulation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Theoretical Computer Science

Cite this

A parallel improvement algorithm for the bipartite subgraph problem. / Lee, Kuo Chun; Funabiki, Nobuo; Takefuji, Yoshiyasu.

In: IEEE Transactions on Neural Networks, Vol. 3, No. 1, 01.1992, p. 139-145.

Research output: Contribution to journalArticle

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