Abstract
During the last decade SAT techniques have become very successful for practice, with important impact in applications such as electronic design automation. DPLL-based clause-learning SAT solvers work surprisingly well on real-world problems from many sources, using a single, fully automatic, push-button strategy. Hence, modeling and using SAT is essentially a declarative task. On the negative side, propositional logic is a very low level language and hence modeling and encoding tools are required. Also, the answer can only be “unsatisfiable” (possibly with a proof) or a model: optimization aspects are not as well studied.
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© 2009 Springer-Verlag Berlin Heidelberg
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Nieuwenhuis, R. (2009). SAT Modulo Theories: Enhancing SAT with Special-Purpose Algorithms. In: Kullmann, O. (eds) Theory and Applications of Satisfiability Testing - SAT 2009. SAT 2009. Lecture Notes in Computer Science, vol 5584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02777-2_1
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DOI: https://doi.org/10.1007/978-3-642-02777-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02776-5
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