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This paper focuses on scattered data fitting problems on spheres. We study the approximation performance of a class of weighted spectral filter algorithms, including Tikhonov regularization, Landaweber iteration, spectral cut-off, and…

Numerical Analysis · Mathematics 2024-10-24 Shao-Bo Lin

This paper introduces a quasi-interpolation operator for scalar- and vector-valued finite element spaces constructed on affine, shape-regular meshes with some continuity across mesh interfaces.This operator gives optimal estimates of the…

Numerical Analysis · Mathematics 2016-10-07 Alexandre Ern , Jean-Luc Guermond

In this article, we give probabilistic versions of Sobolev embeddings on any Riemannian manifold $(M,g)$. More precisely, we prove that for natural probability measures on $L^2(M)$, almost every function belong to all spaces $L^p(M)$,…

Analysis of PDEs · Mathematics 2011-12-01 Nicolas Burq , Gilles Lebeau

We constructed linear algorithms of sampling recovery and cubature formulas on Smolyak grids parametrized by $m \in \mathbb{N}$ of periodic $d$-variate functions having Lipschitz-H\"older mixed smoothness $\alpha > 0$ based on B-spline…

Numerical Analysis · Mathematics 2015-12-29 Dinh Dũng

Higher-order regularization problem formulations are popular frameworks used in machine learning, inverse problems and image/signal processing. In this paper, we consider the computational problem of finding the minimizer of the Sobolev…

Numerical Analysis · Mathematics 2023-10-20 Adrien Weihs , Jalal Fadili , Matthew Thorpe

This paper constructs unique compactly supported functions in Sobolev spaces that have minimal norm, maximal support, and maximal central value, under certain renormalizations. They may serve as optimized basis functions in interpolation or…

Numerical Analysis · Mathematics 2024-09-04 Robert Schaback

The paper contains approximation guarantees for neural networks that are trained with gradient flow, with error measured in the continuous $L_2(\mathbb{S}^{d-1})$-norm on the $d$-dimensional unit sphere and targets that are Sobolev smooth.…

Machine Learning · Computer Science 2023-09-12 G. Welper

We study numerical integration on the unit sphere $\mathbb{S}^2 \subset \mathbb{R}^3$ using equal weight quadrature rules, where the weights are such that constant functions are integrated exactly. The quadrature points are constructed by…

Numerical Analysis · Mathematics 2014-02-17 Johann S. Brauchart , Josef Dick

We investigate the approximation of $d$-variate periodic functions in Sobolev spaces of dominating mixed (fractional) smoothness $s>0$ on the $d$-dimensional torus, where the approximation error is measured in the $L_2-$norm. In other…

Numerical Analysis · Mathematics 2013-12-24 Thomas Kuehn , Winfried Sickel , Tino Ullrich

In this paper a local approximation method on the sphere is presented. As interpolation scheme we consider a partition of unity method, such as the modified spherical Shepard's method, which uses zonal basis functions (ZBFs) plus spherical…

Numerical Analysis · Mathematics 2014-04-08 Alessandra De Rossi

A novel algorithm is proposed for quantitative comparisons between compact surfaces embedded in the three-dimensional Euclidian space. The key idea is to identify those objects with the associated surface measures and compute a weak…

Numerical Analysis · Mathematics 2024-01-17 Kazuki Koga

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

We prove a saturation theorem for linearized shallow ReLU$^k$ neural networks on the unit sphere $\mathbb S^d$. For any antipodally quasi-uniform set of centers, if the target function has smoothness $r>\tfrac{d+2k+1}{2}$, then the best…

Numerical Analysis · Mathematics 2025-11-04 Tong Mao , Jinchao Xu

Kernel interpolation is a fundamental technique for approximating functions from scattered data, with a well-understood convergence theory when interpolating elements of a reproducing kernel Hilbert space. Beyond this classical setting,…

Numerical Analysis · Mathematics 2025-05-19 Toni Karvonen , Gabriele Santin , Tizian Wenzel

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

Functional Analysis · Mathematics 2020-02-04 Youfa Li , Deguang Han , Shouzhi Yang , Ganji Huang

In this paper we analyze the finite element approximation of the Stokes equations with non-smooth Dirichlet boundary data. To define the discrete solution, we first approximate the boundary datum by a smooth one and then apply a standard…

Numerical Analysis · Mathematics 2019-12-12 Ricardo G. Durán , Lucia Gastaldi , Ariel L. Lombardi

In a previous paper we have introduced a new class of radial basis functions that are powerful means to approximate functions by quasi-interpolation. In this article we extend the results to create new ways of approximating functions by…

Numerical Analysis · Mathematics 2025-12-18 M. Buhmann , J. Jódar , M. Rodríguez

In this article, we construct discrete tight frames for $L^2(\mathbb{S}^{d-1})$, $d\geq3$, which consist of localized polynomial wavelets with adjustable degrees of directionality. In contrast to the well studied isotropic case, these…

Classical Analysis and ODEs · Mathematics 2025-12-09 Frederic Schoppert

It is well-known that univariate cubic spline interpolation, if carried out on point sets with fill distance $h$, converges only like ${\cal O}(h^2)$ in $L_2[a,b]$ for functions in $W_2^2[a,b]$ if no additional assumptions are made. But…

Numerical Analysis · Mathematics 2016-07-15 Robert Schaback

The problem of consistency of smoothed particle hydrodynamics (SPH) has demanded considerable attention in the past few years due to the ever increasing number of applications of the method in many areas of science and engineering. A loss…

Numerical Analysis · Mathematics 2016-05-18 Leonardo Di G. Sigalotti , Jaime Klapp , Otto Rendón , Carlos A. Vargas , Franklin Peña-Polo