Norm convergence of multiple ergodic averages for commuting transformations

TL;DR

This paper proves the convergence of multiple ergodic averages for commuting transformations in a probability space, extending previous results to all cases without additional ergodicity assumptions using combinatorial and finitary methods.

Contribution

It establishes the convergence of multiple ergodic averages for any number of commuting transformations using a novel combinatorial and finitary approach.

Findings

Proves $L^2$ convergence for all $l o ext{number of transformations}$

Extends previous results without ergodicity assumptions

Uses combinatorial and hypergraph regularity inspired methods

Abstract

Let $T_1, ..., T_l: X \to X$ be commuting measure-preserving transformations on a probability space $(X, \X, \mu)$. We show that the multiple ergodic averages $\frac{1}{N} \sum_{n=0}^{N-1} f_1(T_1^n x) ... f_l(T_l^n x)$ are convergent in $L^2(X,\X,\mu)$ as $N \to \infty$ for all $f_1,...,f_l \in L^\infty(X,\X,\mu)$; this was previously established for $l=2$ by Conze and Lesigne and for general $l$ assuming some additional ergodicity hypotheses on the maps $T_i$ and $T_i T_j^{-1}$ by Frantzikinakis and Kra (with the $l=3$ case of this result established earlier by Zhang). Our approach is combinatorial and finitary in nature, inspired by recent developments regarding the hypergraph regularity and removal lemmas, although we will not need the full strength of those lemmas. In particular, the $l=2$ case of our arguments are a finitary analogue of those of Conze and Lesigne.