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Abstract
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.
Original language | English |
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Pages (from-to) | 586-614 |
Number of pages | 29 |
Journal | Artificial Intelligence |
Volume | 175 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2011 |
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Dive into the research topics of 'The Extended Global Cardinality Constraint: An Empirical Survey'. Together they form a unique fingerprint.Projects
- 2 Finished
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A Constraint Solver Synthesiser: A Constraint Solver Synthesiser
Miguel, I. J. (PI), Balasubramaniam, D. (CoI), Gent, I. P. (CoI), Kelsey, T. (CoI) & Linton, S. A. (CoI)
1/10/09 → 30/09/14
Project: Standard
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EPSRC: Watched Literals and Learning: Watched Literals and Learning for Constraint Programming
Gent, I. P. (PI) & Miguel, I. J. (CoI)
1/07/07 → 31/03/11
Project: Standard