Inference Rules in Local Search for Max-SAT

Abstract

In the last years, many advances were accomplished in the exact solving of the Max-SAT problem, especially by the definition of new inference rules and a better estimation of lower bounds in branch and bound based methods. However, and oppositely to the SAT problem, fewer works exist on approximate methods for Max-SAT, mainly local search ones which have shown their potency for SAT. In this paper, we illustrate that including inference rules in a classical local search solver for SAT improves its performances when solving the Max-SAT problem. The obtained results confirm the efficiency of our approach.

Should Algorithms for Random SAT and Max-SAT Be Different?

“…• iraNovelty++: The second place in the Max-SAT Evaluation 2013 "Unweighted Random" track. We use the latest binary, provided by its author (Abramé and Habet 2012). • CCLS: CCLS (Luo et al 2015) placed first in the Incomplete Solvers track of the Max-SAT Evaluation 2015 "Unweighted Random" track.…”