A functional central limit theorem for branching random walks, almost sure weak convergence, and applications to random trees

TL;DR

This paper establishes a functional central limit theorem for branching random walks, demonstrating weak convergence to a Gaussian process, and applies it to derive new results on the asymptotic behavior of random trees, including binary search trees.

Contribution

It introduces a novel functional CLT for branching random walks and extends almost sure weak convergence to applications in random trees, improving existing results.

Findings

Weak convergence of the process to a Gaussian analytic function.

Central limit theorems for total path length of random trees.

Almost sure weak convergence results for binary search trees and recursive trees.

Abstract

Let $W_{\infty}(\beta)$ be the limit of the Biggins martingale $W_n(\beta)$ associated to a supercritical branching random walk with mean number of offspring $m$. We prove a functional central limit theorem stating that as $n\to\infty$ the process $$ D_n(u):= m^{\frac 12 n} \left(W_{\infty}\left(\frac{u}{\sqrt n}\right) - W_{n}\left(\frac{u}{\sqrt n}\right) \right) $$ converges weakly, on a suitable space of analytic functions, to a Gaussian random analytic function with random variance. Using this result we prove central limit theorems for the total path length of random trees. In the setting of binary search trees, we recover a recent result of R. Neininger [Refined Quicksort Asymptotics, Rand. Struct. and Alg., to appear], but we also prove a similar theorem for uniform random recursive trees. Moreover, we replace weak convergence in Neininger's theorem by the almost sure weak (a.s.w.) convergence of probability transition kernels. In the case of binary search trees, our result states that $$ L\left\{\sqrt{\frac{n}{2\log n}} \left(EPL_{\infty} - \frac{EPL_n-2n\log n}{n}\right)\Bigg | G_{n}\right\} \to \{\omega\mapsto N_{0,1}\}, \quad \text{a.s.w.},$$ where $EPL_n$ is the external path length of a binary search tree $X_n$ with $n$ vertices, $EPL_{\infty}$ is the limit of the R\'egnier martingale, and $L(\,\cdot\, |G_n)$ denotes the conditional distribution w.r.t. the $\sigma$-algebra $G_n$ generated by $X_1,\ldots,X_n$. A.s.w. convergence is stronger than weak and even stable convergence. We prove several basic properties of the a.s.w. convergence and study a number of further examples in which the a.s.w. convergence appears naturally. These include the classical central limit theorem for Galton-Watson processes and the P\'olya urn.