### Abstract

A parallel algorithm based on the neural network model for finding a near-maximum clique is presented in this paper. A maximum clique of a graph G is a maximum complete subgraph of G where any two vertices are adjacent. The problem of finding a maximum clique is NP-complete. The parallel algorithm requires n processing elements for an n-vertex graph problem. The algorithm is verified by solving 230 different graph problems. The simulation results show that our computation time on a Macintosh IIfx is shorter than that of two better known algorithms on a Cray 2 and an IBM 3090 while the solution quality is similar. The algorithm solves a near-maximum clique problem in nearly constant time on a parallel machine with n processors.

Original language | English |
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Pages (from-to) | 340-344 |

Number of pages | 5 |

Journal | Journal of Parallel and Distributed Computing |

Volume | 14 |

Issue number | 3 |

DOIs | |

Publication status | Published - 1992 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Computer Science Applications
- Hardware and Architecture
- Control and Systems Engineering

### Cite this

*Journal of Parallel and Distributed Computing*,

*14*(3), 340-344. https://doi.org/10.1016/0743-7315(92)90072-U

**A neural network model for finding a near-maximum clique.** / Funabiki, Nubuo; Takefuji, Yoshiyasu; Lee, Kuo Chun.

Research output: Contribution to journal › Article

*Journal of Parallel and Distributed Computing*, vol. 14, no. 3, pp. 340-344. https://doi.org/10.1016/0743-7315(92)90072-U

}

TY - JOUR

T1 - A neural network model for finding a near-maximum clique

AU - Funabiki, Nubuo

AU - Takefuji, Yoshiyasu

AU - Lee, Kuo Chun

PY - 1992

Y1 - 1992

N2 - A parallel algorithm based on the neural network model for finding a near-maximum clique is presented in this paper. A maximum clique of a graph G is a maximum complete subgraph of G where any two vertices are adjacent. The problem of finding a maximum clique is NP-complete. The parallel algorithm requires n processing elements for an n-vertex graph problem. The algorithm is verified by solving 230 different graph problems. The simulation results show that our computation time on a Macintosh IIfx is shorter than that of two better known algorithms on a Cray 2 and an IBM 3090 while the solution quality is similar. The algorithm solves a near-maximum clique problem in nearly constant time on a parallel machine with n processors.

AB - A parallel algorithm based on the neural network model for finding a near-maximum clique is presented in this paper. A maximum clique of a graph G is a maximum complete subgraph of G where any two vertices are adjacent. The problem of finding a maximum clique is NP-complete. The parallel algorithm requires n processing elements for an n-vertex graph problem. The algorithm is verified by solving 230 different graph problems. The simulation results show that our computation time on a Macintosh IIfx is shorter than that of two better known algorithms on a Cray 2 and an IBM 3090 while the solution quality is similar. The algorithm solves a near-maximum clique problem in nearly constant time on a parallel machine with n processors.

UR - http://www.scopus.com/inward/record.url?scp=0026832174&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026832174&partnerID=8YFLogxK

U2 - 10.1016/0743-7315(92)90072-U

DO - 10.1016/0743-7315(92)90072-U

M3 - Article

VL - 14

SP - 340

EP - 344

JO - Journal of Parallel and Distributed Computing

JF - Journal of Parallel and Distributed Computing

SN - 0743-7315

IS - 3

ER -