Leveraging parameterized Chernoff bounds for simplified algorithm analyses

被引：0
|
作者：
Dillencourt, Michael [1 ]
Goodrich, Michael T. [1 ]
Mitzenmacher, Michael [2 ]
机构：
[1] Univ Calif Irvine, Irvine, CA 92697 USA
[2] Harvard Univ, Cambridge, MA USA
来源：
INFORMATION PROCESSING LETTERS | 2024年 / 187卷
基金：
美国国家科学基金会;
关键词：
HEIGHT; FLOYD;
D O I：
10.1016/j.ipl.2024.106516
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we derive parameterized Chernoff bounds and show their applications for simplifying the analysis of some well-known probabilistic algorithms and data structures. The parameterized Chernoff bounds we provide give probability bounds that are powers of two, with a clean formulation of the relation between the constant in the exponent and the relative distance from the mean. In addition, we provide new simplified analyses with these bounds for hash tables, randomized routing, and a simplified, non-recursive adaptation of the Floyd-Rivest selection algorithm.
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页数：7
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