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

Giuliano Grossi; Massimo Marchi; Roberto Posenato

RAIRO - Theoretical Informatics and Applications (2006)

  • Volume: 40, Issue: 2, page 371-388
  • ISSN: 0988-3754

Abstract

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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.

How to cite

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Grossi, Giuliano, Marchi, Massimo, and Posenato, Roberto. "Solving maximum independent set by asynchronous distributed hopfield-type neural networks." RAIRO - Theoretical Informatics and Applications 40.2 (2006): 371-388. <http://eudml.org/doc/249731>.

@article{Grossi2006,
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. },
author = {Grossi, Giuliano, Marchi, Massimo, Posenato, Roberto},
journal = {RAIRO - Theoretical Informatics and Applications},
keywords = {Max independent set; hopfield networks; asynchronous distributed algorithms.; max independent set; Hopfield networks; asynchronous distributed algorithms},
language = {eng},
month = {7},
number = {2},
pages = {371-388},
publisher = {EDP Sciences},
title = {Solving maximum independent set by asynchronous distributed hopfield-type neural networks},
url = {http://eudml.org/doc/249731},
volume = {40},
year = {2006},
}

TY - JOUR
AU - Grossi, Giuliano
AU - Marchi, Massimo
AU - Posenato, Roberto
TI - Solving maximum independent set by asynchronous distributed hopfield-type neural networks
JO - RAIRO - Theoretical Informatics and Applications
DA - 2006/7//
PB - EDP Sciences
VL - 40
IS - 2
SP - 371
EP - 388
AB - 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.
LA - eng
KW - Max independent set; hopfield networks; asynchronous distributed algorithms.; max independent set; Hopfield networks; asynchronous distributed algorithms
UR - http://eudml.org/doc/249731
ER -

References

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