Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Cooling schedules for optimal annealing
Mathematics of Operations Research
A neural network approach to the 3-satisfiability problem
Journal of Parallel and Distributed Computing - Neural Computing
On the complexity of local search
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
On the greedy algorithm for satisfiability
Information Processing Letters
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
A continuous approach to inductive inference
Mathematical Programming: Series A and B
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Constraint partitioning in penalty formulations for solving temporal planning problems
Artificial Intelligence
Boosting local search thanks to CDCL
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
Managing dynamic CSPs with preferences
Applied Intelligence
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GSAT is a randomized local search procedure for solving propositional satisfiability problems. GSAT can solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches, such as the DavisPutnam procedure. This paper presents the results of numerous experiments we have performed with GSAT in order to improve our understanding of its capabilities and limitations. We first characterize the space traversed by GSAT. We will see that for nearly all problem classes we have encountered, the space consists of a steep descent followed by broad flat plateaus. We then compare GSAT with simulated annealing, and show how GSAT can be viewed as an efficient method for executing the lowtemperature tail of an annealing schedule. Finally, we report on extensions to the basic GSAT procedure. We discuss two general, domain-independent extensions that dramatically improve GSAT's performance on structured problems: the use of clause weights, and a way to average in near-solutions when initializing lhe procedure before each try.