Abstract
SAT solvers are now widely used to solve a large variety of problems, including formal verification of systems. SAT problems derived from such applications often exhibit symmetry properties that could be exploited to speed up their solving. Static symmetry breaking is so far the most popular approach to take advantage of symmetries. It relies on a symmetry preprocessor which augments the initial problem with constraints that force the solver to consider only a few configurations among the many symmetric ones.
Highlights
Nowadays, Boolean satisfiability (SAT) is an active research area finding its applications in many contexts such as planning decision [14], hardware and software verification [3], cryptology [19], computational biology [17], etc
State-of-the-art complete solvers of SAT problems are based on the wellknown Conflict Driven Clauses Learning (CDCL) algorithm [18], itself inspired from the Davis–Putnam–Logemann–Loveland algorithm [6]
This paper presented an approach dealing with the symmetries when they appear in SAT problems
Summary
Boolean satisfiability (SAT) is an active research area finding its applications in many contexts such as planning decision [14], hardware and software verification [3], cryptology [19], computational biology [17], etc. A common approach to exploit such symmetries is to pre-compute and enrich the original SAT problem with symmetry breaking predicates (sbp). These added predicates will prevent the solver from visiting equivalent (isomorphic) parts that eventually yield the same results [1,5]. The main advantage of such an approach is to cope with the heavy (and potentially blocking) pre-generation phase of the static-based approaches, and offers opportunities to combine with other dynamic-based approaches, like the symmetry propagation technique [9] It gives more flexibility for adjusting some parameters on the fly. Isomorphic to a part that has been/will be explored
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