Circumspect descent prevails in solving random constraint satisfaction problems
2008 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 105, no 40, 15253-15257 p.Article in journal (Refereed) Published
We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios a; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.
Place, publisher, year, edition, pages
2008. Vol. 105, no 40, 15253-15257 p.
geometry of solutions local search, performance, random K-SAT
Other Biological Topics
IdentifiersURN: urn:nbn:se:kth:diva-8493DOI: 10.1073/pnas.0712263105ISI: 000260360500010ScopusID: 2-s2.0-55749098814OAI: oai:DiVA.org:kth-8493DiVA: diva2:13832
QC 20100903. Uppdaterad från Submitted till Published (20100903)2008-05-192008-05-192010-09-03Bibliographically approved