Propositional reasoning by dimensional reduction: a preliminary report
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Solving SQL Constraints by Incremental Translation to SAT
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Dynamic Orderings for AND/OR Branch-and-Bound Search in Graphical Models
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Artificial Intelligence
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
Dynamic management of heuristics for solving structured CSPs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
JaCk-SAT: a new parallel scheme to solve the satisfiability problem (SAT) based on join-and-check
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Scalable formula decomposition for propositional satisfiability
Proceedings of the Third C* Conference on Computer Science and Software Engineering
AND/OR branch-and-bound search for pure 0/1 integer linear programming problems
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Learning algorithm portfolios for parallel execution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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The general solution of satisfiability problems is NP-Complete. Although state-of-the-art SAT solvers can efficiently obtain the solutions of many real-world instances, there are still a large number of real-world SAT families which cannot be solved in reasonable time. Much effort has been spent to take advantage of the internal structure of SAT instances. Existing decomposition techniques are based on preprocessing the static structure of the original problem. We present a dynamic decomposition method based on hypergraph separators. Integrating the separator decomposition into the variable ordering of a modern SAT solver leads to speedups on large real-world satisfiability problems. Compared with a static decomposition based variable ordering, such as Dtree (Huang and Darwiche, 2003), our approach does not need time to construct the full tree decomposition, which sometimes needs more time than the solving process itself. Our primary focus is to achieve speedups on large real-world satisfiability problems. Our results show that the new solver often outperforms both regular zChaff and zChaff integrated with Dtree decomposition. The dynamic separator decomposition shows promise in that it significantly decreases the number of decisions for some real-world problems.