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    政大機構典藏 > 理學院 > 應用數學系 > 期刊論文 >  Item 140.119/139046


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    题名: Node profiles of symmetric digital search trees: concentration properties
    作者: 符麥克
    Fuchs, Michael
    Drmota, Michael
    Hwang, Hsien-Kuei
    Neininger, Ralph
    贡献者: 應數系
    日期: 2021-05
    上传时间: 2022-02-10 14:59:28 (UTC+8)
    摘要: We give a detailed asymptotic analysis of the profiles of random symmetric digital search trees, which are in close connection with the performance of the search complexity of random queries in such trees. While the expected profiles have been analyzed for several decades, the analysis of the variance turns out to be very difficult and challenging, and requires the combination of several different analytic techniques, including Mellin and Laplace transforms, analytic de‐Poissonization, and Laplace convolutions. Our results imply concentration of the profiles in the range where the mean tends to infinity. Moreover, we also obtain a two‐point concentration for the distributions of the height and the saturation level.
    關聯: Random Struc. Algor., Vol.58, No.3, pp.430-467
    数据类型: article
    DOI 連結: https://doi.org/10.1002/rsa.20979
    DOI: 10.1002/rsa.20979
    显示于类别:[應用數學系] 期刊論文

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