An information-based neural approach to constraint satisfaction

Research output: Contribution to journalArticle


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.


Original languageEnglish
Pages (from-to)1827-1838
Number of pages12
JournalNeural Computation
Issue number8
Publication statusPublished - 2001 Aug
Publication categoryResearch