The practice of approximated consistency for knapsack constraints

被引：0
|
作者：
Sellmann, M [1 ]
机构：
[1] Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA
来源：
PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2004年
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页码：179 / 184
页数：6
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