On the Relative Merits of Simple Local Search Methods for the MAX-SAT Problem

Algorithms based on local search are popular for solving many optimization problems including the maximum satisfiability problem (MAXSAT). With regard to MAXSAT, the state of the art in performance for universal (i.e. non specialized solvers) seems to be variants of Simulated Annealing (SA) and MaxWalkSat (MWS), stochastic local search methods. Local search methods are conceptually simple, and they often provide near optimal solutions. In contrast, it is relatively rare that local search algorithms are analyzed with respect to the worst-case approximation ratios. In the first part of the paper, we build on Mastrolilli and Gambardella’s work [14] and present a worst-case analysis of tabu search for the MAX-k-SAT problem. In the second part of the paper, we examine the experimental performance of determinstic local search algorithms (oblivious and non-oblivious local search, tabu search) in comparison to stochastic methods (SA and MWS) on random 3-CNF and random k-CNF formulas and on...

• Type: Journal
• Year: 2010
• Where: SAT
• Authors: Denis Pankratov, Allan Borodin
• Keywords: Hardware, Local Search, Non-oblivious Local Search, SAT 2010, Tabu Search