DocumentCode
2662454
Title
Neural network solutions to a graph theoretic problem
Author
Shrivastava, Yash ; Dasgupta, Soura ; Reddy, S.M.
fYear
1990
fDate
1-3 May 1990
Firstpage
2528
Abstract
A Hopfield-model-based solution to the node covering problem is considered. It is shown that the sequential algorithm is always convergent, and that for an n -node graph, this convergence occurs in no more than 2n iterations. For the parallel algorithm, a resetting scheme guaranteeing convergence is proposed. Detailed analyses of both schemes are given
Keywords
computational complexity; graph theory; neural nets; optimisation; Hopfield-model-based solution; NP-complete problems; graph theoretic problem; n-node graph; neural network solutions; node covering problem; parallel algorithm; resetting scheme guaranteeing convergence; sequential algorithm; Application software; Codes; Communication networks; Computer networks; Concurrent computing; Neural networks; Parallel algorithms; Parallel processing; Random access memory; Read-write memory;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location
New Orleans, LA
Type
conf
DOI
10.1109/ISCAS.1990.112525
Filename
112525
Link To Document