3,776 research outputs found
The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for different
types of instances of hard combinatorial problems. As a consequence, most
solvers for the propositional satisfiability problem (SAT) expose parameters
that allow them to be customized to a particular family of instances. In the
international SAT competition series, these parameters are ignored: solvers are
run using a single default parameter setting (supplied by the authors) for all
benchmark instances in a given track. While this competition format rewards
solvers with robust default settings, it does not reflect the situation faced
by a practitioner who only cares about performance on one particular
application and can invest some time into tuning solver parameters for this
application. The new Configurable SAT Solver Competition (CSSC) compares
solvers in this latter setting, scoring each solver by the performance it
achieved after a fully automated configuration step. This article describes the
CSSC in more detail, and reports the results obtained in its two instantiations
so far, CSSC 2013 and 2014
On Improving Local Search for Unsatisfiability
Stochastic local search (SLS) has been an active field of research in the
last few years, with new techniques and procedures being developed at an
astonishing rate. SLS has been traditionally associated with satisfiability
solving, that is, finding a solution for a given problem instance, as its
intrinsic nature does not address unsatisfiable problems. Unsatisfiable
instances were therefore commonly solved using backtrack search solvers. For
this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge
to use local search instead to prove unsatisfiability. More recently, two SLS
solvers - Ranger and Gunsat - have been developed, which are able to prove
unsatisfiability albeit being SLS solvers. In this paper, we first compare
Ranger with Gunsat and then propose to improve Ranger performance using some of
Gunsat's techniques, namely unit propagation look-ahead and extended
resolution
Integrating Conflict Driven Clause Learning to Local Search
This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach
for propositional satisfiability. It combines local search and conflict driven
clause learning (CDCL) scheme. Each time the local search part reaches a local
minimum, the CDCL is launched. For SAT problems it behaves like a tabu list,
whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable
sub-formula (MUS). Experimental results show good performances on many classes
of SAT instances from the last SAT competitions
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