Local search with constraint propagation and conflict-based heuristics
Artificial Intelligence
Maintaining Arc-Consistency within Dynamic Backtracking
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems
INFORMS Journal on Computing
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lazy explanations for constraint propagators
PADL'10 Proceedings of the 12th international conference on Practical Aspects of Declarative Languages
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Recent work have exhibited specific structure among combinatorial problem instances that could be used to speed up search or to help users understand the dynamic and static intimate structure of the problem being solved. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure identified within a given problem. The next step is to design algorithms that adaptatively integrate that kind of information during search. We claim in this paper, inspired by previous work on impact-based search strategies for constraint programming, that using an explanation-based constraint solver may lead to collect invaluable information on the intimate dynamic and static structure of a problem instance. We define several impact graphs to be used to design generic search guiding techniques and to identify hidden structures of instances. Finally, we discuss how dedicated OR solving strategies (such as Benders decomposition) could be adapted to constraint programming when specific relationships between variables are exhibited.