A Near-Optimum Parallel Algorithm for Bipartite Subgraph Problem Using the Hopfield Neural Network Learning
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概要
- 論文の詳細を見る
A near-optimum parallel algorithm for bipartite subgraph problem using gradient ascent learning algorithm of the Hopfield neural networks is presented. This parallel algorithm, uses the Hopfield neural network updating to get a near-maximum bipartite subgraph and then performs gradient ascent learning on the Hopfield network to help the network escape from the state of the near-maximum bipartite subgraph until the state of the maximum bipartite subgraph or better one is obtained. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that our algorithm finds the solution quality is superior to that of best existing parallel algorithm. We also test the proposed algorithm on maximum cut problem. The simulation results also show the effectiveness of this algorithm.
- 社団法人電子情報通信学会の論文
- 2002-02-01
著者
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TANG Zheng
Faculty of Engineering, Toyama University
-
CAO Qi-Ping
Tateyama Systems Institute
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WANG Rong-Long
Faculty of Engineering, Fukui University
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Wang Rong-long
Faculty Of Engineering Toyama University
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Tang Zheng
Faculty Of Engineering Miyazaki University
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Wang Rong-long
Faculty Of Engineering Fukui University
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