2001/05/16 by Henrik Jönsson, Henrik Jonsson, Jonsson, Henrik +3
Computer Science · Physics and Astronomy · #Constraint Satisfaction and Optimization #Data Management and Algorithms #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Rough Sets and Fuzzy Logic #cond-mat.dis-nn
paper · pdf · doi:10.48550/arxiv.cond-mat/0105319
13 pages, 3 figures,(to appear in Neural Computation)
arxiv created 2001/05/16 · openalex publication_date 2001/05/16 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.