Henrik Jönsson and Bo Söderberg
An Information-Based Neural Approach to Constraint Satisfaction
Neural Computation 13, 1827-1838 (2001)

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.

LU TP 00-19