Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Maintaining reversible DAC for Max-CSP
Artificial Intelligence
Radio Link Frequency Assignment
Constraints
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A Decomposition Technique for Max-CSP
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Detecting disjoint inconsistent subformulas for computing lower bounds for Max-SAT
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
Propagating soft table constraints
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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In this paper we address Max-CSP, a constraint optimization problem typically solved using a branch and bound scheme. It is well known that the efficiency of branch and bound greatly depends on the quality of the available lower bound. Previous approaches aggregate to the lower bound individual contributions of unassigned variables. In this paper, we augment this approach by adding global contributions of disjoint subsets of unassigned variables, which requires a partition of the set of unassigned variables. Using this idea, we introduce the ipartition-based lower bound. It improves previous approaches based on individual contributions in the sense that our method can be iadded up to previous bounds, possibly increasing their value. We demonstrate our method presenting two new algorithms, PFC-PRDAC and PFC-MPRDAC, which are the natural successors of PFC-RDAC and PFC-MRDAC augmented with our approach. We provide experimental evidence for the superiority of the new algorithms on random problems and real instances of weighted over-constrained problems.