Premium
Node profiles of symmetric digital search trees: Concentration properties
Author(s) -
Drmota Michael,
Fuchs Michael,
Hwang HsienKuei,
Neininger Ralph
Publication year - 2021
Publication title -
random structures and algorithms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.314
H-Index - 69
eISSN - 1098-2418
pISSN - 1042-9832
DOI - 10.1002/rsa.20979
Subject(s) - laplace transform , mellin transform , mathematics , infinity , node (physics) , range (aeronautics) , variance (accounting) , binary search tree , point (geometry) , combinatorics , mathematical analysis , binary tree , geometry , materials science , accounting , structural engineering , engineering , business , composite material
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.