Data structures and network algorithms
Data structures and network algorithms
Introduction to algorithms
A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Graphs and Hypergraphs
Efficient propagators for global constraints
Efficient propagators for global constraints
Tractable cases of the extended global cardinality constraint
CATS '08 Proceedings of the fourteenth symposium on Computing: the Australasian theory - Volume 77
Efficient constraint propagation engines
ACM Transactions on Programming Languages and Systems (TOPLAS)
Generalised arc consistency for the AllDifferent constraint: An empirical survey
Artificial Intelligence
MINION: A Fast, Scalable, Constraint Solver
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Tailoring solver-independent constraint models: a case study with ESSENCE' and MINION
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Advisors for incremental propagation
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Modelling equidistant frequency permutation arrays: an application of constraints to mathematics
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
A hybrid constraint model for the routing and wavelength assignment problem
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Generalized arc consistency for global cardinality constraint
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Watched literals for constraint propagation in minion
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Revisiting the sequence constraint
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
The all different and global cardinality constraints on set, multiset and tuple variables
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Short and long supports for constraint propagation
Journal of Artificial Intelligence Research
The extended global cardinality constraint: an empirical survey (extended abstract)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The Extended Global Cardinality Constraint (EGCC) is a vital component of constraint solving systems, since it is very widely used to model diverse problems. The literature contains many different versions of this constraint, which trade strength of inference against computational cost. In this paper, I focus on the highest strength of inference usually considered, enforcing generalized arc consistency (GAC) on the target variables. This work is an extensive empirical survey of algorithms and optimizations, considering both GAC on the target variables, and tightening the bounds of the cardinality variables. I evaluate a number of key techniques from the literature, and report important implementation details of those techniques, which have often not been described in published papers. Two new optimizations are proposed for EGCC. One of the novel optimizations (dynamic partitioning, generalized from AllDifferent) was found to speed up search by 5.6 times in the best case and 1.56 times on average, while exploring the same search tree. The empirical work represents by far the most extensive set of experiments on variants of algorithms for EGCC. Overall, the best combination of optimizations gives a mean speedup of 4.11 times compared to the same implementation without the optimizations.