69 research outputs found
Cost-optimal constrained correlation clustering via weighted partial Maximum Satisfiability
Peer reviewe
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
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