While global constraints give a broader view on the entire problem and therefore allow more effective constraint propagation, the development of efficient generalized arc-consistency (GAC) algorithms for global constraints is frequently prevented by the fact that the associated decision problems are NP-hard. A prominent example for this is the Knapsack Constraint. On the other hand, there exist approximation algorithms for many NP-hard problems. By introducing the concept of approximated consistency for a special class of global constraints, so-called optimization constraints, we show how existing approximation algorithms can be exploited for the development of efficient filtering algorithms for Knapsack Constraints. As our main result; we show how eGAC for Knapsack and Bounded Knapsack Constraints can be achieved in time O(n log n + n/ε2) or O (n log n + n/ε3), respectively. © Springer-Verlag 2003.
CITATION STYLE
Sellmann, M. (2003). Approximated consistency for knapsack constraints. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2833, 679–693. https://doi.org/10.1007/978-3-540-45193-8_46
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