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arXiv 1612.08052math.CAmath.APmath.DG
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测度的定量Reifenberg定理

Quantitative Reifenberg theorem for measures

Nick Edelen, Aaron Naber, Daniele Valtorta

AI总结:

本文研究基于Jones' $β$-数的$\u211d^n$中测度的Reifenberg定理推广,给出无密度假设下一般测度的有效界及密度假设下的全局结构,验证了结果尖锐性并给出相关应用。

摘要:

我们研究了$\u211d^n$中测度的Reifenberg定理的推广形式,其前提假设涉及Jones的$β$-数——该数可恰当衡量测度的支撑集与被包含在某个子空间中的接近程度。我们的主要结果适用于无密度假设的一般测度,给出了$\u03bc$在一个具有有界Hausdorff测度的闭$k$-可求长集合之外的有效测度界。我们通过示例验证了结果的尖锐性。在额外的密度假设下,可将其转化为$\u03bc$的全局测度界和$k$-可求长结构。应用包括集合与离散测度的定量Reifenberg定理,以及在所有尺度上满足$β$-数估计的测度的Ahlfors正则性上界估计。

英文摘要:

We study generalizations of Reifenberg's Theorem for measures in $\mathbb R^n$ under assumptions on the Jones' $β$-numbers, which appropriately measure how close the support is to being contained in a subspace. Our main results, which holds for general measures without density assumptions, give effective measure bounds on $μ$ away from a closed $k$-rectifiable set with bounded Hausdorff measure. We show examples to see the sharpness of our results. Under further density assumptions one can translate this into a global measure bound and $k$-rectifiable structure for $μ$. Applications include quantitative Reifenberg theorems on sets and discrete measures, as well as upper Ahlfor's regularity estimates on measures which satisfy $β$-number estimates on all scales.

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。

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