The backtracking survey propagation algorithm for solving random K-SAT problems. Marino, R., Parisi, G., & Ricci-Tersenghi, F. Nature Communications, Nature Publishing Group, 2016. cited By 19
The backtracking survey propagation algorithm for solving random K-SAT problems [link]Paper  doi  abstract   bibtex   
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. © The Author(s) 2016.
@ARTICLE{Marino2016,
author={Marino, R. and Parisi, G. and Ricci-Tersenghi, F.},
title={The backtracking survey propagation algorithm for solving random K-SAT problems},
journal={Nature Communications},
year={2016},
volume={7},
doi={10.1038/ncomms12996},
art_number={12996},
note={cited By 19},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990048306&doi=10.1038%2fncomms12996&partnerID=40&md5=6054f1e5bace1e7599d765b30bce01f3},
abstract={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. © The Author(s) 2016.},
publisher={Nature Publishing Group},
issn={20411723},
document_type={Article},
source={Scopus},
}
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