Quantitative affine approximation for UMD targets
Discrete analysis (2016) Cet article a éte moissonné depuis la source Scholastica

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It is shown here that if $(Y,\|\cdot\|_Y)$ is a Banach space in which martingale differences are unconditional (a UMD Banach space) then there exists $c=c(Y)\in (0,\infty)$ with the following property. For every $n\in \mathbb{N}$ and $\varepsilon\in (0,1/2]$, if $(X,\|\cdot\|_X)$ is an $n$-dimensional normed space with unit ball $B_X$ and $f:B_X\to Y$ is a $1$-Lipschitz function then there exists an affine mapping $Λ:X\to Y$ and a sub-ball $B^*=y+ρB_X\subseteq B_X$ of radius $ρ\ge \exp(-(1/\varepsilon)^{cn})$ such that $\|f(x)-Λ(x)\|_Y\le \varepsilon ρ$ for all $x\in B^*$. This estimate on the macroscopic scale of affine approximability of vector-valued Lipschitz functions is an asymptotic improvement (as $n\to \infty$) over the best previously known bound even when $X$ is $\mathbb{R}^n$ equipped with the Euclidean norm and $Y$ is a Hilbert space.
Publié le :
@article{DAS_2016_a13,
     author = {Tuomas Hyt\"onen and Sean Li and Assaf Naor},
     title = {Quantitative affine approximation for {UMD} targets},
     journal = {Discrete analysis},
     year = {2016},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/DAS_2016_a13/}
}
TY  - JOUR
AU  - Tuomas Hytönen
AU  - Sean Li
AU  - Assaf Naor
TI  - Quantitative affine approximation for UMD targets
JO  - Discrete analysis
PY  - 2016
UR  - http://geodesic.mathdoc.fr/item/DAS_2016_a13/
LA  - en
ID  - DAS_2016_a13
ER  - 
%0 Journal Article
%A Tuomas Hytönen
%A Sean Li
%A Assaf Naor
%T Quantitative affine approximation for UMD targets
%J Discrete analysis
%D 2016
%U http://geodesic.mathdoc.fr/item/DAS_2016_a13/
%G en
%F DAS_2016_a13
Tuomas Hytönen; Sean Li; Assaf Naor. Quantitative affine approximation for UMD targets. Discrete analysis (2016). http://geodesic.mathdoc.fr/item/DAS_2016_a13/