Optimal speedup of Las Vegas algorithms
Information Processing Letters
A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
CSPLIB: A Benchmark Library for Constraints
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Constraint Generation via Automated Theory Formation
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Generalised arc consistency for the AllDifferent constraint: An empirical survey
Artificial Intelligence
Propagation via lazy clause generation
Constraints
A fast and simple algorithm for bounds consistency of the all different constraint
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Circuit complexity and decompositions of global constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decompositions of all different, global cardinality and related constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
MiniZinc: towards a standard CP modelling language
CP'07 Proceedings of the 13th 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
Fast set bounds propagation using a BDD-SAT hybrid
Journal of Artificial Intelligence Research
Nogood processing in csps
Solving Talent Scheduling with Dynamic Programming
INFORMS Journal on Computing
Explaining circuit propagation
Constraints
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Lazy clause generation is a powerful approach to reducing search in constraint programming. For use in a lazy clause generation solver, global constraints must be extended to explain themselves. Alternatively they can be decomposed into simpler constraints which already have explanation capability. In this paper we examine different propagation mechanisms for the alldifferent constraint, and show how they can be extended to explain themselves. We compare the different explaining implementations of alldifferent on a variety of problems to determine how explanation changes the trade-offs for propagaton. The combination of global alldifferent propagators with explanation leads to a state-of-the-art constraint programming solution to problems involving alldifferent.