A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Generalized arc consistency for global cardinality constraint
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Using local search to speed up filtering algorithms for some NP-hard constraints
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
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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One way to address the tradeoff between the efficiency and the effectiveness of filtering algorithms for global constraints is as follows: Instead of compromising on the level of consistency, compromise on the frequency at which arc consistency is enforced during the search. In this paper, a method is suggested to determine a reasonable filtering frequency for a given constraint. For dense instances of AllDifferent and its generalization, the Global Cardinality Constraint, let n and m be, respectively, the number of nodes and edges in the variable-value graph. Under the assumption that propagation is random (i.e., each edge removed from the variable-value graph is selected at random), it is shown that recomputing arc consistency only after Θ(m/n) edges were removed results in a speedup while, in the expected sense, filtering effectiveness is comparable to that of enforcing arc consistency at each search step.