Generalizing Boolean Satisfiability III: Implementation

TL;DR

This paper introduces ZAP, a generalized satisfiability engine that leverages problem structure through permutation groups to enhance performance while maintaining high efficiency.

Contribution

It presents the implementation details of ZAP, a novel SAT solver that exploits internal problem structure for improved computational efficiency.

Findings

ZAP outperforms traditional SAT solvers on structured problems.

The use of permutation groups effectively captures problem structure.

Implementation techniques enable practical application of the theoretical ideas.

Abstract

This is the third of three papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high-performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the Boolean representation used; our goal has been to define a representation in which this structure is apparent and can be exploited to improve computational performance. The first paper surveyed existing work that (knowingly or not) exploited problem structure to improve the performance of satisfiability engines, and the second paper showed that this structure could be understood in terms of groups of permutations acting on individual clauses in any particular Boolean theory. We conclude the series by discussing the techniques needed to implement our ideas, and by reporting on their performance on a variety of problem instances.