On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Journal of Computational Physics
Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
Scaling Properties of Pure Random Walk on Random 3-SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
From spin glasses to hard satisfiable formulas
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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An important heuristic in local search algorithms for Satisfiability is focusing, i.e. restricting the selection of flipped variables to those appearing in presently unsatisfied clauses. We consider the behaviour on large randomly generated 3-SAT instances of two focused solution methods: WalkSAT and Focused Metropolis Search. The algorithms turn out to have qualitatively quite similar behaviour. Both are sensitive to the proper choice of their “noise” and “temperature” parameters, but with appropriately chosen values, both achieve solution times that scale linearly in the number of variables even for clauses-to-variables ratios α 4.2. This is much closer to the satisfiability transition threshold αc ≈ 4.267 than has generally been assumed possible for local search algorithms.