Abstract
This paper focuses on developing efficient inference techniques for improving conjunctive normal form (CNF) Boolean satisfiability (SAT) solvers. We analyze a variant of hyper binary resolution from various perspectives: We show that it can simulate the circuit-level technique of structural hashing and how it can be realized efficiently using so called tree-based lookahead. Experiments show that our implementation improves the performance of state-of-the-art CNF-level SAT techniques on combinational equivalent checking instances.
The first author is supported by DARPA contract number N66001-10-2-4087. The first and third authors are supported by Austrian Science Foundation (FWF) NFN Grant S11408-N23 (RiSE), and the second author by Academy of Finland (grants 132812 and 251170).
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Heule, M.J.H., Järvisalo, M., Biere, A. (2013). Revisiting Hyper Binary Resolution. In: Gomes, C., Sellmann, M. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2013. Lecture Notes in Computer Science, vol 7874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38171-3_6
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DOI: https://doi.org/10.1007/978-3-642-38171-3_6
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