Revisiting the Learned Clauses Database Reduction Strategies
This work addresses a key bottleneck in SAT solving for computational logic and verification, though it appears incremental as it builds on existing reduction strategies.
The paper tackles the problem of managing learned clauses in CDCL-based SAT solvers by proposing a Size-Bounded Randomized (SBR) strategy that maintains short clauses and randomly deletes larger ones, resulting in outperforming the state-of-the-art LBD-based strategy on SAT competition instances.
In this paper, we revisit an important issue of CDCL-based SAT solvers, namely the learned clauses database management policies. Our motivation takes its source from a simple observation on the remarkable performances of both random and size-bounded reduction strategies. We first derive a simple reduction strategy, called Size-Bounded Randomized strategy (in short SBR), that combines maintaing short clauses (of size bounded by k), while deleting randomly clauses of size greater than k. The resulting strategy outperform the state-of-the-art, namely the LBD based one, on SAT instances taken from the last SAT competition. Reinforced by the interest of keeping short clauses, we propose several new dynamic variants, and we discuss their performances.