Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
Performance test of local search algorithms using new types of random CNF formulas
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Using Arc weights to improve iterative repair
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
Guided Local Search for Solving SAT and Weighted MAX-SAT Problems
Journal of Automated Reasoning
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
Nonsystematic Search and No-Good Learning
Journal of Automated Reasoning
Parallelizing Local Search for CNF Satisfiability Using Vectorization and PVM
WAE '00 Proceedings of the 4th International Workshop on Algorithm Engineering
Exploiting Partial Knowledge of Satisfying Assignments
WAE '01 Proceedings of the 5th International Workshop on Algorithm Engineering
Automatic Generation of Implied Clauses for SAT
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Combining inference and search for the propositional satisfiability problem
Eighteenth national conference on Artificial intelligence
Parallelizing local search for CNF satisfiability using vectorization and PVM
Journal of Experimental Algorithmics (JEA)
Constraint Models for the Covering Test Problem
Constraints
Exploiting partial knowledge of satisfying assignments
Discrete Applied Mathematics
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Complete local search for propositional satisfiability
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Old resolution meets modern SLS
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Resolution in Max-SAT and its relation to local consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Local search algorithms for partial MAXSAT
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Adaptive clause weight redistribution
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Another complete local search method for SAT
LPAR'05 Proceedings of the 12th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
Constraint metrics for local search
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Constraint-Based approaches to the covering test problem
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
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A primary concern when using local search methods for CNF satisfiability is how to get rid of local minimas. Among many other heuristics, Weighting by Morris (1993) and Selman and Kautz (1993) works overwhelmingly better than others (Cha and Iwama 1995). Weighting increases the weight of each clause which is unsatisfied at a local minima. This paper introduces a more sophisticated weighting strategy, i.e., adding new clauses (ANC) that are unsatisfied at the local minima. As those new clauses, we choose resolvents of the clauses unsatisfied at the local minima and randomly selected neighboring clauses. The idea is that ANC is to make the slope of search space more smooth than the simple weighting. Experimental data show that ANC is faster than simple weighting: (i) When the number of variables is 200 or more, ANC is roughly four to ten times as fast as weighting in terms of the number of search steps. (ii) It might be more important that the divergence of computation time for each try is much smaller in ANC than in weighting. (iii) There are several possible reasons for ANC's superiority, one of which is that ANC returns the same local minima much less frequently than weighting.