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Preprocessing SAT instances can reduce their size considerably. We combine variable elimination with subsumption and selfsubsuming resolution, and show that these techniques not only shrink the formula further than previous preprocessing efforts based on variable elimination, but also decrease runtime of SAT solvers substantially for typical industrial SAT(More)
Last spring, in March 2010, Aaron Bradley published the first truly new bit-level symbolic model checking algorithm since Ken McMillan’s interpolation based model checking procedure introduced in 2003. Our experience with the algorithm suggests that it is stronger than interpolation on industrial problems, and that it is an important algorithm to study(More)
The paper explores several ways to improve the speed and capacity of combinational equivalence checking based on Boolean satisfiability (SAT). State-of-the-art methods use simulation and BDD/SAT sweeping on the input side (i.e. proving equivalence of some internal nodes in a topological order), interleaved with attempts to run SAT on the output (i.e.(More)
SAT solvers are often challenged with very hard problems that remain unsolved after hours of CPU time. The research community meets the challenge in two ways: (1) by improving the SAT solver technology, for example, perfecting heuristics for variable ordering, and (2) by inventing new ways of constructing simpler SAT problems, either using domain specific(More)
We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose a two stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the handengineered features of existing(More)