The practice of approximated consistency for Knapsack constraints

Authors:
Meinolf Sellmann
Affiliations:
Cornell University, Department of Computer Science, Ithaca, NY
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

Citing 2
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Abstract

Knapsack constraints are a key modeling structure in discrete optimization and form the core of many real-life problem formulations. Only recently, a cost-based filtering algorithm for Knapsack constraints was published that is based on some previously developed approximation algorithms for the Knapsack problem. In this paper, we provide an empirical evaluation of approximated consistency for Knapsack constraints by applying it to the Market Split Problem and the Automatic Recording Problem.