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 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.Comment: 13 pages, 3 figures,(to appear in Neural Computation

An information-based neural approach to generic constraint satisfaction

AbstractA novel artificial neural network heuristic (INN) for general constraint satisfaction problems is presented, extending a recently suggested method restricted to boolean variables. In contrast to conventional ANN methods, it employs a particular type of non-polynomial cost function, based on the information balance between variables and constraints in a mean-field setting. Implemented as an annealing algorithm, the method is numerically explored on a testbed of Graph Coloring problems. The performance is comparable to that of dedicated heuristics, and clearly superior to that of conventional mean-field annealing