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Issue No.05 - May (2006 vol.55)
pp: 549-558
ABSTRACT
Identifying and breaking the symmetries of conjunctive normal form (CNF) formulae has been shown to lead to significant reductions in search times. Symmetries in the search space are broken by adding appropriate symmetry-breaking predicates (SBPs) to an SAT instance in CNF. The SBPs prune the search space by acting as a filter that confines the search to nonsymmetric regions of the space without affecting the satisfiability of the CNF formula. For symmetry breaking to be effective in practice, the computational overhead of generating and manipulating SBPs must be significantly less than the runtime savings they yield due to search space pruning. In this paper, we describe a more systematic and efficient construction of SBPs. In particular, we use the cycle structure of symmetry generators, which typically involve very few variables, to drastically reduce the size of SBPs. Furthermore, our new SBP construction grows linearly with the number of relevant variables as opposed to the previous quadratic constructions. Our empirical data suggest that these improvements reduce search runtimes by one to two orders of magnitude on a wide variety of benchmarks with symmetries.
INDEX TERMS
Backtrack Search, clause learning, conjunctive normal form (CNF), graph automorphism, satisfiability (SAT), symmetries.
CITATION
Fadi A. Aloul, Karem A. Sakallah, Igor L. Markov, "Efficient Symmetry Breaking for Boolean Satisfiability", IEEE Transactions on Computers, vol.55, no. 5, pp. 549-558, May 2006, doi:10.1109/TC.2006.75
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