Automatically improving SAT encoding of constraint problems through common subexpression elimination in Savile Row
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The formulation of a Propositional Satisfiability (SAT) problem instance is vital to efficient solving. This has motivated research on preprocessing, and inprocessing techniques where reformulation of a SAT instance is interleaved with solving. Preprocessing and inprocessing are highly effective in extending the reach of SAT solvers, however they necessarily operate on the lowest level representation of the problem, the raw SAT clauses, where higher-level patterns are difficult and/or costly to identify. Our approach is different: rather than reformulate the SAT representation directly, we apply automated reformulations to a higher level representation (a constraint model) of the original problem. Common Subexpression Elimination (CSE) is a family of techniques to improve automatically the formulation of constraint satisfaction problems, which are often highly beneficial when using a conventional constraint solver. In this work we demonstrate that CSE has similar benefits when the reformulated constraint model is encoded to SAT and solved using a state-of-the-art SAT solver. In some cases we observe speed improvements of over 100 times.
Nightingale , P , Spracklen , P & Miguel , I J 2015 , Automatically improving SAT encoding of constraint problems through common subexpression elimination in Savile Row . in G Pesant (ed.) , Principles and Practice of Constraint Programming : 21st International Conference, CP 2015, Cork, Ireland, August 31 -- September 4, 2015, Proceedings . vol. 9255 , Lecture Notes in Computer Science , vol. 9255 , Springer , pp. 330-340 , 21st International Conference on Principles and Practice of Constraint Programming (CP 2015) , Cork , Ireland , 31/08/15 . https://doi.org/10.1007/978-3-319-23219-5_23conference
Principles and Practice of Constraint Programming
© 2015, Springer International Publishing Switzerland. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1007/978-3-319-23219-5_23
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