An information-based neural approach to constraint satisfaction.
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.
Jönsson
H
H
Complex Systems Division, Department of Theoretical Physics, Lund University, S-223 62 Lund, Sweden.
Söderberg
B
B
D000465:Algorithms; D008959:Models, Neurological; D016571:Neural Networks, Computer