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Profiles of Random Trees: Limit Theorems for Random Recursive Trees and Binary Search Trees

机译:随机树的配置文件:随机递归树和二叉搜索树的极限定理

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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 α of the level and the logarithm of tree size lies in [0, e). Convergence of all moments is shown to hold only for α ∈ [0, 1] (with only convergence of finite moments when α ∈ (1, e)). When the limit ratio is 0 or 1 for which the limit laws are both constant, we prove asymptotic normality for α = 0 and a "quicksort type" limit law for α = 1, the latter case having additionally a small range where there is no fixed limit law. Our tools are based on the 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.
机译:我们证明了当级别的极限比α和树大小的对数位于[0,e)时,随机递归树的配置文件(每个级别的节点数)的分布(通过其均值标准化)具有收敛性。显示所有矩的收敛仅对α∈[0,1]成立(当α∈(1,e)时只有有限矩的收敛)。当极限率为0或1且极限律都恒定时,我们证明了α= 0的渐近正态性和α= 1的“快速排序”极限律,后一种情况的范围很小,其中没有固定极限法。我们的工具基于收缩方法和力矩方法。其他类别的树木也有类似现象。我们将我们的工具应用于二叉搜索树,并提供配置文件的完整特征。这些随机树的轮廓代表了具体示例,其分布收敛范围不同于所有矩的收敛范围。

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