Abstract:We characterize all limit laws of the quicksort type random variables defined recursively by X n d = X In + X * n−1−In + T n when the "toll function" T n varies and satisfies general conditions, where (X n), (X * n), (I n , T n) are independent, X n d = X * n , and I n is uniformly distributed over {0,. .. , n − 1}. When the "toll function" T n (cost needed to partition the original problem into smaller subproblems) is small (roughly lim sup n→∞ log E(T n)/ log n ≤ 1/2), X n is asymptotically normally distribu… Show more
“…The same limit law X 0 .1/ also appeared in the total path length (which is P k kX n;k ) of recursive trees (see Dobrow and Fill, 1999), or essentially the total left path length of random binary search trees, and the cost of an in-situ permutation algorithm; see Hwang and Neininger (2002).…”
Section: Corollarymentioning
confidence: 82%
“…By considering N m .˛/ WD m .˛/.m˛C1/=m!, we easily obtain by induction that N m .˛/ D O.K m / for˛2 OE0; 1 (see Hwang and Neininger, 2002), and thus convergence in distribution of X n;k = n;k follows from (6) when˛2 OE0; 1.…”
Section: Moment Convergence (6)mentioning
confidence: 89%
“…From the recurrence (32) and the estimate (34), we deduce, by an induction similar to that used for (27), that…”
We prove convergence in distribution for the profile (the number of nodes at each level), normalized by its mean, of random recursive trees when the limit ratio˛of the level and the logarithm of tree size lies in OE0; e/. Convergence of all moments is shown to hold only for˛2 OE0; 1 (with only convergence of finite moments when˛2 .1; e/). When the limit ratio is 0 or 1 for which the limit laws are both constant, we prove asymptotic normality for˛D 0 and a "quicksort type" limit law for˛D 1, the latter case having additionally a small range where there is no fixed limit law. Our tools are based on contraction method and method of moments. Similar phenomena also hold for other classes of trees; we apply our tools to binary search trees and give a complete characterization of the profile. The profiles of these random trees represent concrete examples for which the range of convergence in distribution differs from that of convergence of all moments.
“…The same limit law X 0 .1/ also appeared in the total path length (which is P k kX n;k ) of recursive trees (see Dobrow and Fill, 1999), or essentially the total left path length of random binary search trees, and the cost of an in-situ permutation algorithm; see Hwang and Neininger (2002).…”
Section: Corollarymentioning
confidence: 82%
“…By considering N m .˛/ WD m .˛/.m˛C1/=m!, we easily obtain by induction that N m .˛/ D O.K m / for˛2 OE0; 1 (see Hwang and Neininger, 2002), and thus convergence in distribution of X n;k = n;k follows from (6) when˛2 OE0; 1.…”
Section: Moment Convergence (6)mentioning
confidence: 89%
“…From the recurrence (32) and the estimate (34), we deduce, by an induction similar to that used for (27), that…”
We prove convergence in distribution for the profile (the number of nodes at each level), normalized by its mean, of random recursive trees when the limit ratio˛of the level and the logarithm of tree size lies in OE0; e/. Convergence of all moments is shown to hold only for˛2 OE0; 1 (with only convergence of finite moments when˛2 .1; e/). When the limit ratio is 0 or 1 for which the limit laws are both constant, we prove asymptotic normality for˛D 0 and a "quicksort type" limit law for˛D 1, the latter case having additionally a small range where there is no fixed limit law. Our tools are based on contraction method and method of moments. Similar phenomena also hold for other classes of trees; we apply our tools to binary search trees and give a complete characterization of the profile. The profiles of these random trees represent concrete examples for which the range of convergence in distribution differs from that of convergence of all moments.
“…This is a "Quicksort"-like recurrence, which has been treated in various works (see Hwang and Neininger, 2002, Kirschenhofer and Prodinger, 1998, Kirschenhofer, Prodinger and Martínez , 1997, and Panholzer, 2003, Panholzer and Prodinger, 1998, and Prodinger, 1995. We only outline here the strategy: One begins by differencing (n − 1) E(Y n−1 ) from n E(Y n ).…”
Abstract.We investigate the Randić index of random binary trees under two standard probability models: the one induced by random permutations and the Catalan (uniform). In both cases the mean and variance are computed by recurrence methods and shown to be asymptotically linear in the size of the tree. The recursive nature of binary search trees lends itself in a natural way to application of the contraction method, by which a limit distribution (for a suitably normalized version of the index) is shown to be Gaussian. The Randić index (suitably normalized) is also shown to be normally distributed in binary Catalan trees, but the methodology we use for this derivation is singularity analysis of formal generating functions.
“…These include in particular asymptotic expressions for the means and variances, as well as limit laws for the scaled quantities, and large deviation inequalities, see Hennequin [22,23], Régnier [42], Rösler [43,45], McDiarmid and Hayward [11], Bruhn [3], and for a detailed survey the book of Mahmoud [28]. For the number of exchanges B n the mean and variance were for general t ∈ N 0 studied in Hennequin [23], Chern and Hwang [5] refined the analysis of the mean, and Hwang and Neininger [25] gave a limit law for the standard case t = 0.…”
The contraction method for recursive algorithms is extended to the multivariate analysis of vectors of parameters of recursive structures and algorithms. We prove a general multivariate limit law which also leads to an approach to asymptotic covariances and correlations of the parameters. As an application the asymptotic correlations and a bivariate limit law for the number of key comparisons and exchanges of median-of-(2t + 1) Quicksort is given. Moreover, for the Quicksort programs analyzed by Sedgewick the exact order of the standard deviation and a limit law follow, considering all the parameters counted by Sedgewick.
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