Weight Redistribution for Unweighted MAX-SAT

Abstract

Abstract. Many real-world problems are over-constrained and require search techniques adapted to optimising cost functions rather than searching for consistency. This makes the MAX-SAT problem an important area of research for the satisfiability (SAT) community. In this study we perform an empirical analysis of several of the best performing SAT local search techniques in the domain of unweighted MAX-SAT. In particular, we test two of the most recently developed SAT clause weight redistribution algorithms, DDFW and DDFW + , against three more well-known techniques (RSAPS, AdaptNovelty + and PAWS). Based on an empirical study across a range of previously studied problems we conclude that DDFW is the most promising algorithm in terms of robust average performance.

Dynamic Initial Weight Assignment for MaxSAT

“…The only modification to be considered instead of searching for a complete solution (where all the clauses are satisfied), DLS solvers are modified to keep track of the best partial solution reached (in other words, keeping track of the number of maximum satisfied clauses reached so far) and at which time. For instance, an unweighted MaxSAT problem's clauses are equally treated (regardless of whether they must be satisfied as partial MaxSAT), so an SAT solver could be applied to solve unweighted MaxSAT [41]. However, the unweighted MaxSAT problem is impractical when handling real-world problems encoded in CNF, since real-world problems often have different types of constraints.…”