The backtracking survey propagation algorithm for solving random K-SAT problems
2016 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 7, 12996Article in journal (Refereed) Published
Discrete combinatorial optimization has a central role in many scientific disciplines, however, for hard problems we lack linear time algorithms that would allow us to solve very large instances. Moreover, it is still unclear what are the key features that make a discrete combinatorial optimization problem hard to solve. Here we study random K-satisfiability problems with K=3,4, which are known to be very hard close to the SAT-UNSAT threshold, where problems stop having solutions. We show that the backtracking survey propagation algorithm, in a time practically linear in the problem size, is able to find solutions very close to the threshold, in a region unreachable by any other algorithm. All solutions found have no frozen variables, thus supporting the conjecture that only unfrozen solutions can be found in linear time, and that a problem becomes impossible to solve in linear time when all solutions contain frozen variables.
Place, publisher, year, edition, pages
Nature Publishing Group, 2016. Vol. 7, 12996
IdentifiersURN: urn:nbn:se:kth:diva-195260DOI: 10.1038/ncomms12996ISI: 000385537300002ScopusID: 2-s2.0-84990048306OAI: oai:DiVA.org:kth-195260DiVA: diva2:1046882
FunderSwedish Research Council, 621-2012-2982EU, European Research Council, 694925
QC 201611152016-11-152016-11-022016-11-15Bibliographically approved