An information-based neural approach to constraint satisfaction

Research output: Contribution to journalArticle


title = "An information-based neural approach to constraint satisfaction",
abstract = "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.",
author = "Henrik J{\"o}nsson and Bo S{\"o}derberg",
year = "2001",
month = aug,
doi = "10.1162/08997660152469369",
language = "English",
volume = "13",
pages = "1827--1838",
journal = "Neural Computation",
issn = "1530-888X",
publisher = "MIT Press",
number = "8",