A parallel improvement algorithm for the bipartite subgraph problem

Author

Lee, Kuo Chun ; Funabiki, Nobuo ; Takefuji, Yoshiyasu

Author_Institution

Cirrus Logic Inc., Fremont, CA, USA

Volume

3

Issue

1

fYear

1992

fDate

1/1/1992 12:00:00 AM

Firstpage

139

Lastpage

145

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

Keywords

graph theory; iterative methods; neural nets; parallel algorithms; K-partite subgraph problem; NP-complete problem; bipartite subgraph problem; edges; iteration steps; maximum neural network model; parallel improvement algorithm; Artificial neural networks; Biological system modeling; Bipartite graph; Concurrent computing; Constraint optimization; Helium; Mathematical model; NP-complete problem; Neural networks; Neurons;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.105427

Filename

105427