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Solving maximum independent set by asynchronous distributed hopfield-type neural networks

Published online by Cambridge University Press:  20 July 2006

Giuliano Grossi ,
Massimo Marchi  and
Roberto Posenato
Giuliano Grossi
Affiliation:
Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico 39, 20135 Milano, Italy; grossi@dsi.unimi.it
Massimo Marchi
Affiliation:
Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico 39, 20135 Milano, Italy; grossi@dsi.unimi.it
Roberto Posenato
Affiliation:
Dipartimento di Informatica, Università degli Studi di Verona, strada le grazie 15, 37134 Verona, Italy.
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Abstract

We propose a heuristic for solving the maximum independent set problem for a set of processors in a network with arbitrary topology. We assume an asynchronous model of computation and we use modified Hopfield neural networks to find high quality solutions. We analyze the algorithm in terms of the number of rounds necessary to find admissible solutions both in the worst case (theoretical analysis) and in the average case (experimental Analysis). We show that our heuristic is better than the greedy one at 1% significance level.

Keywords

Max independent sethopfield networksasynchronous distributed algorithms.
Type
Research Article
Information
RAIRO - Theoretical Informatics and Applications , Volume 40 , Issue 2: Alberto Bertoni: Climbing summits , April 2006 , pp. 371 - 388
Copyright
© EDP Sciences, 2006

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