Arc and path consistence revisited
Artificial Intelligence
Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
From local to global consistency
Artificial Intelligence
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Fast parallel constraint satisfaction
Artificial Intelligence
Characterising tractable constraints
Artificial Intelligence
On the minimality and global consistency of row-convex constraint networks
Journal of the ACM (JACM)
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Synthesizing constraint expressions
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Constraint Representation for Propagation
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Increasing functional constraints need to be checked only once
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Incrementally solving functional constraints
Eighteenth national conference on Artificial intelligence
An Elimination Algorithm for Functional Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Efficient Algorithms for Functional Constraints
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Solving functional constraints by variable substitution
Theory and Practice of Logic Programming
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We present new complexity results on the class of 0/1/All constraints. The central idea involves functional elimination, a general method of elimination whose focus is on the subclass of functional constraints. One result is that for the subclass of "All" constraints, strong n-consistency and minimality is achievable in O(en) time, where e, n are the number of constraints and variables. The main result is that we can solve 0/1/All constraints in O(e(d + n)) time, where d is the domain size. This is an improvement over known results, which are O(ed(d+n)). Furthermore, our algorithm also achieves strong n-consistency and minimality.