A generic arc-consistency algorithm and its specializations
Artificial Intelligence
A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Artificial Intelligence
The Difference All-Difference Makes
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Solving the Round Robin Problem Using Propositional Logic
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
On Forward Checking for Non-binary Constraint Satisfaction
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
A theoretical comparison of selected csp solving and modeling techniques
A theoretical comparison of selected csp solving and modeling techniques
The Langford's Problem: A Challenge for Parallel Resolution of CSP
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Dual Models of Permutation Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Automated reformulation of specifications by safe delay of constraints
Artificial Intelligence
SampleSearch: Importance sampling in presence of determinism
Artificial Intelligence
Compositional load test generation for software pipelines
Proceedings of the 2012 International Symposium on Software Testing and Analysis
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When writing a constraint program, we have to decide what to make the decision variable, and how to represent the constraints on these variables. In many cases, there is considerable choice for the decision variables. For example, with permutation problems, we can choose between a primal and a dual representation. In the dual representation, dual variables stand for the primal values, whilst dual values stand for the primal variables. By means of channelling constraints, a combined model can have both primal and dual variables. In this paper, we perform an extensive theoretical and empirical study of these different models. Our results will aid constraint programmers to choose a model for a permutation problem. They also illustrate a general methodology for comparing different constraint models.