Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
Local Search Characteristics of Incomplete SAT Procedures
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Improved Exponential-Time Algorithm for k-SAT
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
A new method for solving hard satisfiability 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
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Journal of Symbolic Computation
Worst-case study of local search for MAX-k-SAT
Discrete Applied Mathematics - The renesse issue on satisfiability
Heuristic-Based Backtracking Relaxation for Propositional Satisfiability
Journal of Automated Reasoning
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
Variable dependency in local search: prevention Is better than cure
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Random walk with continuously smoothed variable weights
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
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In this paper we present a new randomized algorithm for SAT combining unit clause elimination and local search. The algorithm is inspired by two randomized algorithms having the best current worstcase upper bounds ([9] and [11,12]). Despite its simplicity, our algorithm performs well on many common benchmarks (we present results of its empirical evaluation). It is also probabilistically approximately complete.