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We present a method to gradually compute a smaller and smaller unsatisfiable core of a propositional formula by minimizing proofs of unsatisfiability. The goal is to compute a minimal unsatisfiable core that is relatively small compared to other minimal unsatisfiable cores of the same formula. We try to achieve this goal by postponing deletion of arbitrary clauses from the formula as long as possible---in contrast to existing minimal unsatisfiable core algorithms. We applied this method to reduce the smallest known unit-distance graph with chromatic number 5 from 553 vertices and 2720 edges to 529 vertices and 2670 edges.
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Laderman discovered a scheme for computing the product of two 3 x 3 matrices using only 23 multiplications in 1976. Since then, some more such schemes were proposed, but nobody knows how many such schemes there are and whether there exist schemes with fewer than 23 multiplications. In this paper we present two independent SAT-based methods for finding new schemes using 23 multiplications. Both methods allow computing a few hundred new schemes individually, and many thousands when combined. Local search SAT solvers outperform CDCL solvers consistently in this application.more » « less
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Mutilated chessboard problems have been called a “tough nut to crack” for automated reasoning. They are, for instance, hard for resolution, resulting in exponential runtime of current SAT solvers. Although there exists a well-known short argument for solving mutilated chessboard problems, this argument is based on an abstraction that is challenging to discover by automated-reasoning techniques. In this paper, we present another short argument that is much easier to compute and that can be expressed within the recent (clausal) PR proof system for propositional logic. We construct short clausal proofs of mutilated chessboard problems using this new argument and validate them using a formally-verified proof checker.more » « less
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Satisfaction-Driven Clause Learning (SDCL) is a recent SAT solving paradigm that aggressively trims the search space of possible truth assignments. To determine if the SAT solver is currently exploring a dispensable part of the search space, SDCL uses the so-called positive reduct of a formula: The positive reduct is an easily solvable propositional formula that is satisfiable if the current assignment of the solver can be safely pruned from the search space. In this paper, we present two novel variants of the positive reduct that allow for even more aggressive pruning. Using one of these variants allows SDCL to solve harder problems, in particular the well-known Tseitin formulas and mutilated chessboard problems. For the first time, we are able to generate and automatically check clausal proofs for large instances of these problems.more » « less
