A Parallel Improvement Algorithm for the Bipartite Subgraph Problem

Kuo Chun Lee, Nobuo Funabiki, Yoshiyasu Takefuji

研究成果: Article査読

52 被引用数 (Scopus)

抄録

Since McCulloch and Pitts proposed an artificial neuron model in 1943, several neuron models have been investigated. This paper proposes 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 our 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.

本文言語English
ページ(範囲)139-145
ページ数7
ジャーナルIEEE Transactions on Neural Networks
3
1
DOI
出版ステータスPublished - 1992 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信
  • 人工知能

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