Abstract
The efficiency of branch and bound-based algorithms for Max-CSP depends largely on the quality of the available lower bound. An existing approach for lower bound computation aggregates individual contributions of unassigned variables. In this paper, we generalize this approach. Instead of aggregating individual contributions, we aggregate global contributions of disjoint subsets of unassigned variables, which requires a partition of the set of unassigned variables. Using this idea, we introduce the partition-based lower bound, which is superior to previous ones based on individual contributions. Interestingly, this lower bound includes elements already existing in the literature (IC and DAC). We present two algorithms, PFC-PRDAC and PFC-MPRDAC, which are the natural successors of PFC-RDAC and PFC-MRDAC using this new bound. We provide experimental evidence for the superiority of the new algorithms on random problems and real instances of weighted over-constrained problems.
This research is supported by the Spanish CICYT project TIC96-0721-C02-02.
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Larrosa, J., Meseguer, P. (1999). Partition-Based Lower Bound for Max-CSP. In: Jaffar, J. (eds) Principles and Practice of Constraint Programming – CP’99. CP 1999. Lecture Notes in Computer Science, vol 1713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48085-3_22
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DOI: https://doi.org/10.1007/978-3-540-48085-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66626-4
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