GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
Journal of Global Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Complete local search for propositional satisfiability
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Understanding the power of clause learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Adding new clauses for faster local search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Disco - Novo - GoGo: integrating local search and complete search with restarts
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
GUNSAT: a greedy local search algorithm for unsatisfiability
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Local search algorithms are one of the effective methods for solving hard combinatorial problems. However, a serious problem of this approach is that the search often traps at local optima. At AAAI 2004, Fang and Ruml proposed a novel approach which makes local optima disappeared. The basic idea is that, at each local optimal point during the search, the value of the objective function (a local gradient function) at that point is changed by adding some information into the database. Once no more local optima exist, the local search can always find a global optimal. In this paper, along the same approach of Fang and Ruml, we propose a different objective function based on an ordering of propositional variables. Based on this ordering, ordered resolution is performed at each local optimal point and the resolvent is added into the database. This resolvent always increases the value of the objective function so that the local optimal point disappears after a finite number of steps. Preliminary experimental results show that our method and Fang and Ruml’s method have better performances in different areas.