Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T04:19:40.318Z Has data issue: false hasContentIssue false

Integer sequences with big gaps and the pointwise ergodic theorem

Published online by Cambridge University Press:  01 October 1999

ROGER L. JONES
Affiliation:
Department of Mathematics, DePaul University, 2219 North Kenmore, Chicago, IL 60614, USA (e-mail: [email protected])
MICHAEL LACEY
Affiliation:
Department of Mathematics, Georgia Technical University, Atlanta, GA 30323, USA (e-mail: [email protected])
MÁTÉ WIERDL
Affiliation:
Department of Mathematical Sciences, University of Memphis, Memphis, TN 38152, USA (e-mail: [email protected])

Abstract

First, we show that there exists a sequence $(a_n)$ of integers which is a good averaging sequence in $L^2$ for the pointwise ergodic theorem and satisfies $$ \frac{a_{n+1}}{a_n}>e^{(\log n)^{-1-\epsilon}} $$ for $n>n(\epsilon)$. This should be contrasted with an earlier result of ours which says that if a sequence $(a_n)$ of integers (or real numbers) satisfies $$ \frac{a_{n+1}}{a_n}>e^{(\log n)^{-\frac{1}{2}+\epsilon}} $$ for some positive $\epsilon$, then it is a bad averaging sequence in $L^2$ for the pointwise ergodic theorem.

Another result of the paper says that if we select each integer $n$ with probability $1/n$ into a random sequence, then, with probability 1, the random sequence is a bad averaging sequence for the mean ergodic theorem. This result should be contrasted with Bourgain's result which says that if we select each integer $n$ with probability $\sigma_n$ into a random sequence, where the sequence $(\sigma_n)$ is decreasing and satisfies $$ \lim_{t\to\infty}\frac{\sum_{n\le t}\sigma_n}{\log t}=\infty, $$ then, with probability 1, the random sequence is a good averaging sequence for the mean ergodic theorem.

Type
Research Article
Copyright
1999 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)