On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Optimal speedup of Las Vegas algorithms
Information Processing Letters
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
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
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
Captain Jack: new variable selection heuristics in local search for SAT
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Diversification and determinism in local search for satisfiability
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Improving stochastic local search for SAT with a new probability distribution
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
An empirical study of optimal noise and runtime distributions in local search
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Principles of stochastic local search
UC'07 Proceedings of the 6th international conference on Unconventional Computation
Local search for Boolean Satisfiability with configuration checking and subscore
Artificial Intelligence
Comprehensive score: towards efficient local search for SAT with long clauses
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Stochastic local search solvers for SAT made a large progress with the introduction of probability distributions like the ones used by the SAT Competition 2011 winners Sparrow2010 and EagleUp. These solvers though used a relatively complex decision heuristic, where probability distributions played a marginal role. In this paper we analyze a pure and simple probability distribution based solver probSAT, which is probably one of the simplest SLS solvers ever presented. We analyze different functions for the probability distribution for selecting the next flip variable with respect to the performance of the solver. Further we also analyze the role of make and break within the definition of these probability distributions and show that the general definition of the score improvement by flipping a variable, as make minus break is questionable. By empirical evaluations we show that the performance of our new algorithm exceeds that of the SAT Competition winners by orders of magnitude.