Algorithms for a Class of Isotonic Regression Problems

被引：0
|
作者：
P. M. Pardalos
G. Xue
机构：
[1] Department of Industrial and Systems Engineering,
[2] 303 Weil Hall,undefined
[3] University of Florida,undefined
[4] Gainesville,undefined
[5] FL 32611,undefined
[6] USA. pardalos@ufl.edu.,undefined
[7] Department of Computer Science,undefined
[8] The University of Vermont,undefined
[9] Burlington,undefined
[10] VT 05405,undefined
[11] USA. xue@cs.uvm.edu.,undefined
来源：
Algorithmica | 1999年 / 23卷
关键词：
Key words. Isotonic regression, Binomial heap, Linear time algorithms.;
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学科分类号：
摘要：
The isotonic regression problem has applications in statistics, operations research, and image processing. In this paper a general framework for the isotonic regression algorithm is proposed. Under this framework, we discuss the isotonic regression problem in the case where the directed graph specifying the order restriction is a directed tree with n vertices. A new algorithm is presented for this case, which can be regarded as a generalization of the PAV algorithm of Ayer et al. Using a simple tree structure such as the binomial heap, the algorithm can be implemented in O(n log n) time, improving the previously best known O(n2) time algorithm. We also present linear time algorithms for special cases where the directed graph is a path or a star.
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页码：211 / 222
页数：11
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