An Information-Based Neural Approach to Constraint Satisfaction
Disordered Systems and Neural Networks
2007-05-23 v1
Abstract
A novel artificial neural network approach to constraint satisfaction problems is presented. Based on information-theoretical considerations, it differs from a conventional mean-field approach in the form of the resulting free energy. The method, implemented as an annealing algorithm, is numerically explored on a testbed of K-SAT problems. The performance shows a dramatic improvement to that of a conventional mean-field approach, and is comparable to that of a state-of-the-art dedicated heuristic (Gsat+Walk). The real strength of the method, however, lies in its generality -- with minor modifications it is applicable to arbitrary types of discrete constraint satisfaction problems.
Cite
@article{arxiv.cond-mat/0105319,
title = {An Information-Based Neural Approach to Constraint Satisfaction},
author = {Henrik Jonsson and Bo Soderberg},
journal= {arXiv preprint arXiv:cond-mat/0105319},
year = {2007}
}
Comments
13 pages, 3 figures,(to appear in Neural Computation)