Improving Variable Selection Process in Stochastic Local Search for Propositional Satisfiability

Stochastic Local Search for SMT: Combining Theory Solvers with WalkSAT

“…A few attempts have been made in order to enhance SLS algorithms with techniques borrowed from CDCL solvers (e.g. [6,4]). In particular, Belov and Stachniak [6] propose two techniques that exploit the search history to improve the variable selection process of the classic SLS procedures for SAT.…”

“…[6,4]). In particular, Belov and Stachniak [6] propose two techniques that exploit the search history to improve the variable selection process of the classic SLS procedures for SAT. They modify the WalkSAT schema by adding a database (DB) that represents a set of constraints that help to guide the search process.…”

“…ψ ∧ η |= l) and updates the current total truth assignment µ accordingly during a single search step. We refer the reader to [6] for a more detailed explanation.…”

“…The implementation is done on top of MATHSAT4 [7], using part of its preprocessor its LA(Q)-solver [9] and lots of its features. We have implemented two versions, each using one between two SLS-based SAT solvers: UBC-SAT 4 [17] and UBCSAT++ 5 [6]. UBCSAT is a SLS platform providing a very-wide range of SLS algorithms for SAT (including the WalkSAT family), with a very flexible architecding the WalkSAT family), with a very flexible architecture.…”

“…Second, we analyze the differences between the interaction of a T -solver with a CDCL-based and a SLS-based SAT solver, and we introduce and discuss a group of optimization techniques aimed at improving the synergy between an SLS solver and the T -solver. Third, we present an implementation of WALKSMT with the optimization techniques above, which is based on the integration of the UBCSAT [17] and UBC-SAT++ [6] SLS solvers with the LA(Q)-solver of MATHSAT [7]. Finally, we perform an extensive experimental evaluation of our implementation.…”