An information-based neural approach to constraint satisfaction

Summary, in English

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.

• Författare: Henrik Jönsson; Bo Söderberg
• Avdelning/ar: Beräkningsbiologi och biologisk fysik - Har omorganiserats
• Publiceringsår: 2001-08
• Språk: Engelska
• Sidor: 1827-1838
• Publikation/Tidskrift/Serie: Neural Computation
• Volym: 13
• Issue: 8
• Länkar: http://dx.doi.org/10.1162/08997660152469369
• Dokumenttyp: Artikel i tidskrift
• Förlag: MIT Press
• Aktiv: Published
• ISBN/ISSN/Övrigt: ISSN: 0899-7667