arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2103.01700math.DSmath.CA
收藏

Estimates on the dimension of self-similar measures with overlaps

De-Jun Feng, Zhou Feng

英文摘要:

In this paper, we provide an algorithm to estimate from below the dimension of self-similar measures with overlaps. As an application, we show that for any $ β\in(1,2) $, the dimension of the Bernoulli convolution $ μ_β$ satisfies \[ \dim (μ_β) \geq 0.9804085,\] which improves a previous uniform lower bound $0.82$ obtained by Hare and Sidorov \cite{HareSidorov2018}. This new uniform lower bound is very close to the known numerical approximation $ 0.98040931953\pm 10^{-11}$ for $\dim μ_{β_3}$, where $ β_{3} \approx 1.839286755214161$ is the largest root of the polynomial $ x^{3}-x^{2}-x-1$. Moreover, the infimum $\inf_{β\in (1,2)}\dim (μ_β) $ is attained at a parameter $β_*$ in a small interval \[ (β_{3} -10^{-8}, β_{3} + 10^{-8}).\] When $β$ is a Pisot number, we express $\dim(μ_β)$ in terms of the measure-theoretic entropy of the equilibrium measure for certain matrix pressure function, and present an algorithm to estimate $\dim (μ_β)$ from above as well.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑