An information-based neural approach to constraint satisfaction
(2001) In Neural Computation 13(8). p.1827-1838- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a6809e4a-0f9a-4fc4-9331-d056dbb65e96
- author
- Jönsson, Henrik LU and Söderberg, Bo LU
- organization
- publishing date
- 2001-08
- type
- Contribution to journal
- publication status
- published
- in
- Neural Computation
- volume
- 13
- issue
- 8
- pages
- 12 pages
- publisher
- MIT Press
- external identifiers
-
- scopus:0035434198
- ISSN
- 0899-7667
- DOI
- 10.1162/08997660152469369
- language
- English
- LU publication?
- yes
- id
- a6809e4a-0f9a-4fc4-9331-d056dbb65e96
- date added to LUP
- 2016-10-03 19:09:03
- date last changed
- 2022-12-14 02:58:11
@article{a6809e4a-0f9a-4fc4-9331-d056dbb65e96, abstract = {{<p>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.</p>}}, author = {{Jönsson, Henrik and Söderberg, Bo}}, issn = {{0899-7667}}, language = {{eng}}, number = {{8}}, pages = {{1827--1838}}, publisher = {{MIT Press}}, series = {{Neural Computation}}, title = {{An information-based neural approach to constraint satisfaction}}, url = {{http://dx.doi.org/10.1162/08997660152469369}}, doi = {{10.1162/08997660152469369}}, volume = {{13}}, year = {{2001}}, }