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It is well known that the ratio of the number of clauses to the number of variables in a random k-SAT instance is highly correlated with the instance’s empirical hardness. We consider the problem of identifying such features of random SAT instances automatically using machine learning. We describe and analyze models for three SAT solvers—kcnfs, oksolver and(More)
Inspired by the success of recent work in the constraint programming community on typical-case complexity, in [3] we developed a new methodology for using machine learning to study empirical hardness of hard problems on realistic distributions. In [2] we demonstrated that this new approach can be used to construct practical algorithm portfolios. In brief,(More)
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