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The approximation numbers of the $L_2$-embedding of mixed order Sobolev functions on the $d$-torus are well studied. They are given as the nonincreasing rearrangement of the $d$-th tensor power of the approximation number sequence in the…

Numerical Analysis · Mathematics 2017-10-10 David Krieg

We continue the research on the asymptotic and preasymptotic decay of singular numbers for tensor product Hilbert-Sobolev type embeddings in high dimensions with special emphasis on the influence of the underlying dimension $d$. The main…

Numerical Analysis · Mathematics 2020-10-01 Thomas Kuehn , Winfried Sickel , Tino Ullrich

Using tools from the theory of operator ideals and s-numbers, we develop a general approach to transfer estimates for $L_2$ -approximation of Sobolev functions into estimates for $L_\infty$-approximation, with precise control of all…

Functional Analysis · Mathematics 2015-05-12 Fernando Cobos , Thomas Kühn , Winfried Sickel

We consider the order of convergence for linear and nonlinear Monte Carlo approximation of compact embeddings from Sobolev spaces of dominating mixed smoothness defined on the torus $\mathbb{T}^d$ into the space $L_{\infty}(\mathbb{T}^d)$…

Numerical Analysis · Mathematics 2018-03-02 Glenn Byrenheid , Robert J. Kunsch , Van Kien Nguyen

We combine a periodization strategy for weighted $L_{2}$-integrands with efficient approximation methods in order to approximate multivariate non-periodic functions on the high-dimensional cube $\left[-\frac{1}{2},\frac{1}{2}\right]^{d}$.…

Numerical Analysis · Mathematics 2021-01-22 Robert Nasdala , Daniel Potts

In this paper, we study the approximation problem for functions in the Gaussian-weighted Sobolev space $W^\alpha_p(\mathbb{R}^d, \gamma)$ of mixed smoothness $\alpha \in \mathbb{N}$ with error measured in the Gaussian-weighted space…

Functional Analysis · Mathematics 2023-09-29 Van Kien Nguyen

Let $\Omega_i\subset\mathbb{R}^{n_i}$, $i=1,\ldots,m$, be given domains. In this article, we study the low-rank approximation with respect to $L^2(\Omega_1\times\dots\times\Omega_m)$ of functions from Sobolev spaces with dominating mixed…

Numerical Analysis · Mathematics 2022-08-18 Michael Griebel , Helmut Harbrecht , Reinhold Schneider

In this paper, we reveal a new connection between approximation numbers of periodic Sobolev type spaces, where the smoothness weights on the Fourier coefficients are induced by a (quasi-)norm $\|\cdot\|$ on $\mathbb{R}^d$, and entropy…

Numerical Analysis · Mathematics 2016-09-13 Thomas Kühn , Sebastian Mayer , Tino Ullrich

In this paper, we obtain the preasymptotic and asymptotic behavior and strong equivalences of the approximation numbers of the embeddings from the anisotropic Sobolev spaces $W_2^{\bf R}(\Bbb T^d)$ to $L_2(\Bbb T^d)$. We also get the…

Numerical Analysis · Mathematics 2016-08-11 JIa Chen , Heping Wang

In this paper, we investigate optimal linear approximations ($n$-approximation numbers ) of the embeddings from the Sobolev spaces $H^r\ (r>0)$ for various equivalent norms and the Gevrey type spaces $G^{\alpha,\beta}\ (\alpha,\beta>0)$ on…

Classical Analysis and ODEs · Mathematics 2017-01-16 Jia Chen , Heping Wang

In this paper, we investigate the approximation problem for functions in Gaussian Sobolev spaces $W^s_p(\mathbb{R}^d, \gamma)$ of smoothness $s > 0$, where the approximation error is measured in the Gaussian Lebesgue space…

Functional Analysis · Mathematics 2026-04-21 Van Kien Nguyen

In this paper we present results on asymptotic characteristics of multivariate function classes in the uniform norm. Our main interest is the approximation of functions with mixed smoothness parameter not larger than $1/2$. Our focus will…

Functional Analysis · Mathematics 2021-11-01 Vladimir Temlyakov , Tino Ullrich

We established exact in order estimates an approximation of the Sobolev classes $W^{\boldsymbol{r}}_{p,\boldsymbol{\alpha}}(\mathbb{T}^d)$ of periodic functions of many variables with a bounded dominating mixed derivative. The approximation…

Classical Analysis and ODEs · Mathematics 2024-10-17 K. V. Pozharska , A. S. Romanyuk , S. Ya. Yanchenko

We study the recovery of multivariate functions from reproducing kernel Hilbert spaces in the uniform norm. Our main interest is to obtain preasymptotic estimates for the corresponding sampling numbers. We obtain results in terms of the…

Numerical Analysis · Mathematics 2024-10-29 Kateryna Pozharska , Tino Ullrich

We study integration and $L_2$-approximation on countable tensor products of function spaces of increasing smoothness. We obtain upper and lower bounds for the minimal errors, which are sharp in many cases including, e.g., Korobov, Walsh,…

Numerical Analysis · Mathematics 2021-09-21 M. Gnewuch , M. Hefter , A. Hinrichs , K. Ritter , G. W. Wasilkowski

Sobolev embeddings, of arbitrary order, are considered into function spaces on domains of $\mathbb R^n$ endowed with measures whose decay on balls is dominated by a power $d$ of their radius. Norms in arbitrary rearrangement-invariant…

Functional Analysis · Mathematics 2019-12-10 Andrea Cianchi , Luboš Pick , Lenka Slavíková

We consider approximation problems for a special space of d variate functions. We show that the problems have small number of active variables, as it has been postulated in the past using concentration of measure arguments. We also show…

Numerical Analysis · Mathematics 2012-01-25 Markus Hegland , Greg W. Wasilkowski

This paper describes an extension of Fourier approximation methods for multivariate functions defined on the torus $\mathbb{T}^d$ to functions in a weighted Hilbert space $L_{2}(\mathbb{R}^d, \omega)$ via a multivariate change of variables…

Numerical Analysis · Mathematics 2019-12-20 Robert Nasdala , Daniel Potts

The Sobolev space $H^{s}(\mathbb{R}^{d})$, where $s > d/2$, is an important function space that has many applications in various areas of research. Attributed to the inertia of a measuring instrument, it is desirable in sampling theory to…

Functional Analysis · Mathematics 2017-07-06 Youfa Li , Deguang Han

In this paper, we introduce a method known as polynomial frame approximation for approximating smooth, multivariate functions defined on irregular domains in $d$ dimensions, where $d$ can be arbitrary. This method is simple, and relies only…

Numerical Analysis · Mathematics 2020-05-27 Ben Adcock , Daan Huybrechs
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