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"... and then just encode it to SAT."

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Encoding problems to Boolean satisfiability is a common technique in state-of-the-art program verification tools and theorem provers. Every problem can be encoded in many different ways and the choice of encoding can have a major effect on performance. Unfortunately there are few tools, theoretical or practical, to compare encodings or design better ones. This talk introduces an ongoing program of work which will address this problem. We show how the language of abstract interpretation can be used to capture the notion of the propagating power of an encoding and how this can be used to automatically generate optimal encodings. This not only improves solver performance but also gives a language to explore problem difficulty and understand encodings.

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