Efficient conflict driven learning in a boolean satisfiability solver

TLDR: This paper generalizes various conflict driven learning strategies in terms of different partitioning schemes of the implication graph to re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes.

Abstract: One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.