Radio Link Frequency Assignment
Constraints
Intelligent Domain Splitting for CSPs with Ordered Domains
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Virtual Arc consistency for weighted CSP
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Domain-splitting generalized nogoods from restarts
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
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Branch and bound is an effective technique for solving constraint optimization problems (COP's). However, its search space expands very rapidly as the domain sizes of the problem variables grow. In this paper, we present an algorithm that clusters the values of a variable's domain into sets. Branch and bound can then branch on these sets of values rather than on individual values, thereby reducing the branching factor of its search space. The aim of our clustering algorithm is to construct a collection of sets such that branching on these sets will still allow effective bounding. In conjunction with the reduced branching factor, the size of the explored search space is thus significantly reduced. We test our method and show empirically that it can yield significant performance gains over existing state-of-the-art techniques.