2019
DOI: 10.48550/arxiv.1906.01487
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Gradient bounds for radial maximal functions

Emanuel Carneiro,
Cristian González-Riquelme

Abstract: In this paper we study the regularity properties of certain maximal operators of convolution type at the endpoint p = 1, when acting on radial data. In particular, for the heat flow maximal operator and the Poisson maximal operator, when the initial datumis a radial function, we show that the associated maximal function u * is weakly differentiable andThis establishes the analogue of a recent result of H. Luiro for the uncentered Hardy-Littlewood maximal operator, now in a centered setting with smooth kernels.… Show more

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Cited by 3 publications
(6 citation statements)
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“…This recent result inspired the study the regularity theory of maximal functions in higher dimensions when restricted to radial data. For instance, in [6] the analogue of this result for some centered kernels is proved.…”
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confidence: 87%
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“…This recent result inspired the study the regularity theory of maximal functions in higher dimensions when restricted to radial data. For instance, in [6] the analogue of this result for some centered kernels is proved.…”
mentioning
confidence: 87%
“…When working on the circle S 1 , an adaptation of the proof of Aldaz and Pérez Lázaro [2] yields Var( Mf ) ≤ Var(f ), where Var(f ) denotes the total variation of the function f . Recently, Carneiro and the author [6] settled affirmatively this question in the case of polar functions. The general case remains open.…”
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confidence: 92%
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“…Extending such a statement to more general Sobolev functions of several variables is a difficult open problem, which has inspired many results in related topics. For instance, slightly stronger bounds have been proved for maximal operators with more special convolution kernels (see [7], [3], [4] and [20]), the continuity of the mapping has been studied in [17] and [6], and a part of the techniques used for continuity, also relevant for the current paper, have been extended to p = 1 in [11].…”
Section: Introductionmentioning
confidence: 99%