Square Functions and Variational Estimates for Ritt Operators on $L^1$

Abstract

Let $T$ be a bounded operator. We say $T$ is a Ritt operator if $\sup_n n\lVert T^n-T^{n+1}\rVert<\infty$. It is know that when $T$ is a positive contraction and a Ritt operator in $L^p$, $1<p<\infty$, then for any integer $m\ge 1$, the square function \[\Big( \sum_n n^{2m-1} |T^n(I-T)^{m}f|^2 \Big)^{1/2}\] defines a bounded operator \cite{LeMX-Vq} in $L^p$. In this work, we extend the theory to the endpoint case $p=1$, showing that if $T$ is a Ritt operator on $L^1$, then the generalized square function \[Q_{α,s,m}f=\Big( \sum_n n^α |T^n(I-T)^mf|^s \Big)^{1/s}\] is bounded on $L^1$ for $α+1<sm$. In the specific setting where $T$ is a convolution operator of the form $T_μ=\sum_k μ(k) U^kf$, with $μ$ a probability measure on $\mathbb Z$ and $U$ the composition operator induced by an invertible, ergodic measure preserving transformation, we provide sufficient conditions on $μ$ under which the square function $Q_{2m-1,2,m}$ is of weak type (1,1), for all integers $m\ge 1$. We also establish bounds for variational and oscillation norms, $\lVert n^β T^n(1-T)^r\rVert_{v(s)}$ and $\lVert n^β T^n(1-T)^r\rVert_{o(s)}$, for Ritt operators, highlighting endpoint behavior.

Theorems & Definitions (31)

• Definition 1.1
• Definition 1.2
• Definition 1.3
• Definition 1.4
• Theorem 1.5
• Theorem 1.6
• Theorem 1.7
• Remark
• Theorem 1.8
• Theorem 1.9