### 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 language | English |
---|---|

Pages (from-to) | 139-145 |

Number of pages | 7 |

Journal | IEEE Transactions on Neural Networks |

Volume | 3 |

Issue number | 1 |

DOIs | |

Publication status | Published - 1992 Jan |

Externally published | Yes |

### Fingerprint

### 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

*IEEE Transactions on Neural Networks*,

*3*(1), 139-145. https://doi.org/10.1109/72.105427

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

Research output: Contribution to journal › Article

*IEEE Transactions on Neural Networks*, vol. 3, no. 1, pp. 139-145. https://doi.org/10.1109/72.105427

}

TY - JOUR

T1 - A parallel improvement algorithm for the bipartite subgraph problem

AU - Lee, Kuo Chun

AU - Funabiki, Nobuo

AU - Takefuji, Yoshiyasu

PY - 1992/1

Y1 - 1992/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0026628404&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026628404&partnerID=8YFLogxK

U2 - 10.1109/72.105427

DO - 10.1109/72.105427

M3 - Article

AN - SCOPUS:0026628404

VL - 3

SP - 139

EP - 145

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

SN - 2162-237X

IS - 1

ER -