On Improving Local Search for Unsatisfiability

TL;DR

This paper compares two stochastic local search solvers for proving unsatisfiability and introduces improvements to Ranger by incorporating techniques from Gunsat, enhancing its effectiveness in unsatisfiability proofs.

Contribution

The paper compares Ranger and Gunsat solvers and proposes integrating Gunsat's techniques into Ranger to improve its unsatisfiability proving capabilities.

Findings

Ranger and Gunsat have different strengths in proving unsatisfiability.

Incorporating Gunsat's techniques improves Ranger's performance.

Enhanced Ranger shows better success rates in unsatisfiability proofs.

Abstract

Stochastic local search (SLS) has been an active field of research in the last few years, with new techniques and procedures being developed at an astonishing rate. SLS has been traditionally associated with satisfiability solving, that is, finding a solution for a given problem instance, as its intrinsic nature does not address unsatisfiable problems. Unsatisfiable instances were therefore commonly solved using backtrack search solvers. For this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge to use local search instead to prove unsatisfiability. More recently, two SLS solvers - Ranger and Gunsat - have been developed, which are able to prove unsatisfiability albeit being SLS solvers. In this paper, we first compare Ranger with Gunsat and then propose to improve Ranger performance using some of Gunsat's techniques, namely unit propagation look-ahead and extended resolution.