On Dedicated CDCL Strategies for PB Solvers

TL;DR

This paper explores specialized CDCL strategies tailored for pseudo-Boolean solvers, demonstrating that adapting these strategies to PB constraints can significantly enhance solver performance on decision and optimization problems.

Contribution

It introduces and evaluates novel adaptations of CDCL strategies specifically for PB constraints, improving solver efficiency.

Findings

Dedicated strategies improve solver performance

Significant gains on decision problems

Performance improvements on optimization problems

Abstract

Current implementations of pseudo-Boolean (PB) solvers working on native PB constraints are based on the CDCL architecture which empowers highly efficient modern SAT solvers. In particular, such PB solvers not only implement a (cutting-planes-based) conflict analysis procedure, but also complementary strategies for components that are crucial for the efficiency of CDCL, namely branching heuristics, learned constraint deletion and restarts. However, these strategies are mostly reused by PB solvers without considering the particular form of the PB constraints they deal with. In this paper, we present and evaluate different ways of adapting CDCL strategies to take the specificities of PB constraints into account while preserving the behavior they have in the clausal setting. We implemented these strategies in two different solvers, namely Sat4j (for which we consider three configurations) and RoundingSat. Our experiments show that these dedicated strategies allow to improve, sometimes significantly, the performance of these solvers, both on decision and optimization problems.