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)
Generalised arc consistency for the AllDifferent constraint: An empirical survey
Artificial Intelligence
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
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
Generalized arc consistency for global cardinality constraint
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
The extended global cardinality constraint: An empirical survey
Artificial Intelligence
Expected-Case analysis for delayed filtering
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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The Extended Global Cardinality Constraint (EGCC) is an important 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.