![]() We implemented our heuristics in state-of-the-art solvers (MapleCOMSPS and MapleLCMDistChronotBT for SAT competition 2018 application benchmarks) and CryptoMiniSAT, Glucose and MapleSAT (in the context of cryptographic benchmarks), against 4 other initialization methods. ![]() We performed extensive experiments to evaluate the efficacy of our BMM-based heuristics on SAT competition 2018 application and hard cryptographic benchmarks. Further, the BMM-based MapleCOMSPS significantly out-perform the same solver using all other initialization methods by 12 additional instances solved and better average runtime, over the SAT 2018 competition benchmark. ![]() On the cryptographic benchmark, BMM-based solvers out-perform all other initialization methods. We perform extensive experiments to evaluate the efficacy of our BMM-based heuristic against 4 other initialization methods (random, survey propagation, Jeroslow-Wang, and default) in state-of-the-art solvers, MapleCOMSPS and MapleLCMDistChronotBT over the SAT competition 2018 application benchmark, as well as the best-known solvers in the cryptographic category, namely, CryptoMiniSAT, Glucose and MapleSAT. This probability distribution is then used by the solver to initialize its search. ![]() At the start of a solver run, our BMM-based methods compute a posterior probability distribution for an assign- ment to the variables of the input formula after analyzing its clauses. The initialization problem can be stated as follows: given a SAT formula φ, compute an initial order over the variables of φ and values/polarity for these variables such that the runtime of SAT solvers on input φ is minimized. ![]() In this paper, we present a Bayesian Moment Matching (BMM) based method aimed at solving the initialization problem in Boolean SAT solvers. ![]()
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