Diversifying a parallel SAT solver with bayesian moment matching
In Symposium on dependable software engineering theories, tools and applications
Abstract
In this paper, we present a Bayesian Moment Matching (BMM) in-processing technique for Conflict-Driven Clause-Learning (CDCL) SAT solvers. BMM is a probabilistic algorithm which takes as input a Boolean formula in conjunctive normal form and a prior on a possible satisfying assignment, and outputs a posterior for a new assignment most likely to maximize the number of satisfied clauses. We invoke this BMM method, as an in-processing technique, with the goal of updating the polarity and branching activity scores. The key insight underpinning our method is that Bayesian reasoning is a powerful way to guide the CDCL search procedure away from fruitless parts of the search space of a satisfiable Boolean formula, and towards those regions that are likely to contain satisfying assignments.