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Stochastic local search for SMT: combining theory solvers with WalkSAT. (English) Zbl 1348.68227
Tinelli, Cesare (ed.) et al., Frontiers of combining systems. 8th international symposium, FroCoS 2011, Saarbrücken, Germany, October 5–7, 2011. Proceedings. Berlin: Springer (ISBN 978-3-642-24363-9/pbk). Lecture Notes in Computer Science 6989. Lecture Notes in Artificial Intelligence, 163-178 (2011).
Summary: A dominant approach to Satisfiability Modulo Theories (SMT) relies on the integration of a Conflict-Driven-Clause-Learning (CDCL) SAT solver and of a decision procedure able to handle sets of atomic constraints in the underlying theory \(\mathcal{T}\) (\(\mathcal{T}\)-solver). In pure SAT, however, Stochastic Local-Search (SLS) procedures sometimes are competitive with CDCL SAT solvers on satisfiable instances. Thus, it is a natural research question to wonder whether SLS can be exploited successfully also inside SMT tools.
In this paper we investigate this issue. We first introduce a general procedure for integrating a SLS solver of the WalkSAT family with a \(\mathcal{T}\)-solver. Then we present a group of techniques aimed at improving the synergy between these two components. Finally we implement all these techniques into a novel SLS-based SMT solver for the theory of linear arithmetic over the rationals, combining UBCSAT/UBCSAT++ and MathSAT, and perform an empirical evaluation on satisfiable instances. The results confirm the potential of the approach.
For the entire collection see [Zbl 1223.68012].

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68T15 Theorem proving (deduction, resolution, etc.) (MSC2010)
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