Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

August 1, 2001, Vol. 13, No. 8, Pages 1827-1838
(doi: 10.1162/08997660152469369)
© 2001 Massachusetts Institute of Technology
An Information-Based Neural Approach to Constraint Satisfaction
Article PDF (107.16 KB)
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 over 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.