Accelerating Boolean Satisfiability (SAT) solving by common subclause elimination

Forrest Sheng Bao, Chris Gutierrez, Jeriah Jn Charles-Blount, Yaowei Yan, Yuanlin Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Boolean Satisfiability (SAT) is an important problem in many domains. Modern SAT solvers have been widely used in important industrial applications including automated planning and verification. To solve more problems in real applications, new techniques are needed to speed up SAT solving. Inspired by the success of common subexpression elimination in programming languages and other related areas, we study the impact of common subclause elimination (CSE) on SAT solving. Intensive experiments on many SAT solvers and benchmarks with 48-h timeout show that CSE can consistently improve SAT solving. Up to 5% more SAT13 instances can be solved after CSE. LZW-based CSE shows the best overall performance, particularly in the category of application benchmarks. A potential use of this result is that one may consider the heuristic of applying CSE to boost SAT solver performance on real life applications. Because of many possible ways to improve the benefit of CSE, we hope future research can unleash the full potential of CSE in SAT solving.

Original languageEnglish
Pages (from-to)439-453
Number of pages15
JournalArtificial Intelligence Review
Issue number3
StatePublished - Mar 1 2018


  • Application benchmarks
  • Boolean Satisfiability
  • Common subclause elimination


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