mlsanityData to 14 Oct 2026

On Dedicated CDCL Strategies for PB Solvers

arXiv 2109.01013 ↗First posted 2 Sep 2021cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 38 references that are in the map. First posted on arXiv 2 Sep 2021.

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3 of 38 references.

References · 3

  1. 2014Clause-Learning Algorithms with Many Restarts and Bounded-Width Resolutioncited by 7
  2. 2020On Weakening Strategies for PB Solverscited by 2
  3. 2020On Irrelevant Literals in Pseudo-Boolean Constraint Learningcited by 2

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