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We obtain sharp embeddings from the Sobolev space $W^{k,2}_0(-1,1)$ into the space $L^1(-1,1)$ and determine the extremal functions. This improves on a previous estimate of the sharp constants of these embeddings due to Kalyabin.

Functional Analysis · Mathematics 2024-11-18 Raul Hindov , Shahaf Nitzan , Jan-Fredrik Olsen , Eskil Rydhe

There exists a plethora of parametric models for positive definite kernels, and their use is ubiquitous in disciplines as diverse as statistics, machine learning, numerical analysis, and approximation theory. Usually, the kernel parameters…

Machine Learning · Statistics 2025-01-06 Xavier Emery , Emilio Porcu , Moreno Bevilacqua

Embedding theorems for symmetric functions without zero boundary condition have been studied on flat Riemannian manifolds, such as the Euclidean space. However, these theorems have only been established on hyperbolic spaces for functions…

Analysis of PDEs · Mathematics 2025-03-24 João Marcos do Ó , Guozhen Lu , Raoní Ponciano

In this paper we introduce a generalization of the classical $\Leb_2(\Rd)$-based Sobolev spaces with the help of a vector differential operator $\mathbf{P}$ which consists of finitely or countably many differential operators $P_n$ which…

Numerical Analysis · Mathematics 2011-09-02 Qi Ye

In this paper, we present the basic concepts of the geometric theory of composition operators on Sobolev spaces. The main objects of the theory are topological mappings which generate bounded embedding operators on Sobolev spaces by the…

Analysis of PDEs · Mathematics 2024-11-21 Vladimir Gol'dshtein , Alexander Ukhlov

We investigate the existence of embeddings of shearlet coorbit spaces associated to weighted mixed $L^p$-spaces into classical Sobolev spaces in dimension three by using the description of coorbit spaces as decomposition spaces. This…

Functional Analysis · Mathematics 2019-04-03 Hartmut Führ , René Koch

This work extends the study of mean field equations arising in two-dimensional (2D) turbulence by introducing generalized weighted Sobolev operators. Employing variational methods, particularly the mountain pass theorem and a refined…

Analysis of PDEs · Mathematics 2024-10-22 Rômulo Damasclin Chaves dos Santos

This short note investigates the compact embedding of degenerate matrix weighted Sobolev spaces into weighted Lebesgue spaces. The Sobolev spaces explored are defined as the abstract completion of Lipschitz functions in a bounded domain…

Analysis of PDEs · Mathematics 2019-08-16 Dario D. Monticelli , Scott Rodney

In the present paper, we investigate whether an embedding of a decomposition space $\mathcal{D}\left(\mathcal{Q},L^{p},Y\right)$ into a given Sobolev space $W^{k,q}(\mathbb{R}^{d})$ exists. As special cases, this includes embeddings into…

Functional Analysis · Mathematics 2016-01-12 Felix Voigtlaender

Motivated by a class of nonlinear equations of interest for string theory, we introduce Sobolev spaces on arbitrary locally compact abelian groups and we examine some of their properties. Specifically, we focus on analogs of the Sobolev…

Mathematical Physics · Physics 2012-08-16 Przemysław Górka , Enrique G. Reyes

This work provides theoretical foundations for kernel methods in the hyperspherical context. Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the Sobolev spaces associated with kernels defined over…

Machine Learning · Statistics 2022-11-18 Simon Hubbert , Emilio Porcu , Chris. J. Oates , Mark Girolami

We develop novel learning rates for conditional mean embeddings by applying the theory of interpolation for reproducing kernel Hilbert spaces (RKHS). We derive explicit, adaptive convergence rates for the sample estimator under the…

Machine Learning · Statistics 2026-04-09 Prem Talwai , Ali Shameli , David Simchi-Levi

We establish sharp Sobolev embedding properties within a broad class of compact matrix quantum groups of Kac type under the polynomial growth or the rapid decay property of their duals. Main examples are duals of polynomially growing…

Operator Algebras · Mathematics 2018-11-27 Sang-Gyun Youn

In this note we revisit a result in [9], where we established nonlocal isoperimetric inequalities and the related embeddings for Besov spaces adapted to a class of H\"ormander operators of Kolmogorov-type. We provide here a new proof which…

Analysis of PDEs · Mathematics 2023-04-21 Nicola Garofalo , Giulio Tralli

We prove the sharp regularizing estimates for the gain term of the Boltzmann collision operator including hard sphere, hard potential and Maxwell molecule models. Our new estimates characterize both regularization and convolution properties…

Analysis of PDEs · Mathematics 2019-06-07 Jin-Cheng Jiang

The main goal of this paper is to introduce a new fractional anisotropic Sobolev space with variable exponent where the basic qualitative properties (completeness, separability, reflexivity, ...) are established, including the continuous…

Analysis of PDEs · Mathematics 2024-10-07 Elhoussine Azroul , Abdelkrim Barbara , Nezha Kamali , Mohammed Shimi

We address the study of nonlocal gradients defined through general radial kernels $\rho$. Our investigation focuses on the properties of the associated function spaces, which depend on the characteristics of the kernel function.…

Analysis of PDEs · Mathematics 2024-02-27 José Carlos Bellido , Carlos Mora-Corral , Hidde Schönberger

We consider function spaces of Besov, Triebel-Lizorkin, Bessel-potential and Sobolev type on $\R^d$, equipped with power weights $w(x) = |x|^\gamma$, $\gamma>-d$. We prove two-weight Sobolev embeddings for these spaces. Moreover, we…

Functional Analysis · Mathematics 2012-02-10 Martin Meyries , Mark Veraar

We consider periodically perforated unbounded open sets and prove existence of extremals for the relevant sharp Poincar\'e-Sobolev embedding constant. The existence result holds no matter the shape or the regularity of the hole: it is…

Analysis of PDEs · Mathematics 2025-11-26 Lorenzo Brasco , Luca Briani , Francesca Prinari

In machine learning or statistics, it is often desirable to reduce the dimensionality of a sample of data points in a high dimensional space $\mathbb{R}^d$. This paper introduces a dimensionality reduction method where the embedding…

Machine Learning · Computer Science 2021-11-30 Michaël Fanuel , Antoine Aspeel , Jean-Charles Delvenne , Johan A. K. Suykens