Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Minimum Cardinality Matrix Decomposition into Consecutive-Ones Matrices: CP and IP Approaches
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Learning effective search heuristics
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Propagation = lazy clause generation
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Minimizing the maximum number of open stacks by customer search
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Lazy clause generation reengineered
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Why cumulative decomposition is not as bad as it sounds
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Explaining the cumulative propagator
Constraints
Reducing chaos in SAT-like search: finding solutions close to a given one
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
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Lazy Clause Generation is a powerful approach to reducing search in Constraint Programming. This is achieved by recording sets of domain restrictions that previously led to failure as new clausal propagators called nogoods. This dramatically reduces the search and provides orders of magnitude speedups on a wide range of problems. Current implementations of Lazy Clause Generation only allows solvers to learn and utilize nogoods within an individual problem. This means that everything the solver learns will be forgotten as soon as the current problem is finished. In this paper, we show how Lazy Clause Generation can be extended so that nogoods learned from one problem can be retained and used to significantly speed up the solution of other, similar problems.