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The paper extends an earlier result of G.V.~Kalachev and the author (Sb. Math. 2019 or arXiv:1712.08836) on the existence of a maximizer of convolution operator acting between two Lebesgue spaces on $R^n$ with kernel from some $L_q$,…

Functional Analysis · Mathematics 2022-08-19 Sergey Sadov

We obtain Gaussian upper bounds for heat kernels of higher order differential operators with Dirichlet boundary conditions on bounded domains in $\R^N$. The bounds exhibit explicitly the nature of the spatial decay of the heat kernel close…

Analysis of PDEs · Mathematics 2012-08-01 Narinder Claire

A symmetrization inequality of Rogers and of Brascamp-Lieb-Luttinger states that for a certain class of multilinear integral expressions, among tuples of sets of prescribed Lebesgue measures, tuples of balls centered at the origin are among…

Classical Analysis and ODEs · Mathematics 2017-12-04 Michael Christ , Kevin O'Neill

Weighted $L^p-L^r$ inequalities with arbitrary measurable non-negative weights for positive quasilinear integral operators with Oinarov's kernel on the semiaxis are characterized. Application to the boundedness of maximal operator in the…

Functional Analysis · Mathematics 2016-11-23 Dmitrii V. Prokhorov , Vladimir D. Stepanov

On Euclidean spaces, the Gaussian kernel is one of the most widely used kernels in applications. It has also been used on non-Euclidean spaces, where it is known that there may be (and often are) scale parameters for which it is not…

Machine Learning · Computer Science 2023-02-22 Nathaël Da Costa , Cyrus Mostajeran , Juan-Pablo Ortega

We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized…

Statistics Theory · Mathematics 2010-09-14 Felix Abramovich , Vadim Grinshtein

Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establish learning rates…

Machine Learning · Statistics 2017-02-28 Muhammad Farooq , Ingo Steinwart

The Gaussian kernel plays a central role in machine learning, uncertainty quantification and scattered data approximation, but has received relatively little attention from a numerical analysis standpoint. The basic problem of finding an…

Numerical Analysis · Mathematics 2021-04-02 Toni Karvonen , Chris J. Oates , Mark Girolami

We study positive definite kernels pulled back along a finite family of self-maps under a subinvariance inequality for the associated branching operator. Iteration produces an increasing kernel tower with defect kernels. Under diagonal…

Probability · Mathematics 2026-02-03 James Tian

The finite-rank Lieb-Thirring inequality provides an estimate on a Riesz sum of the $N$ lowest eigenvalues of a Schr\"odinger operator $-\Delta-V(x)$ in terms of an $L^p(\mathbb{R}^d)$ norm of the potential $V$. We prove here the existence…

Analysis of PDEs · Mathematics 2023-05-12 Rupert L. Frank , David Gontier , Mathieu Lewin

We establish an effective upper bound for the Brascamp-Lieb constant associated to a weighted family of linear maps.

Classical Analysis and ODEs · Mathematics 2026-04-10 Timothée Bénard , Weikun He

In this article we investigate an action of some operators (not necessary to be linear or sublinear) in the so-called (Bilateral) Grand Lebesgue Spaces (GLS), in particular, double weight Fourier operators, maximal operators, imbedding…

Functional Analysis · Mathematics 2011-04-18 E. Ostrovsky L. Sirota

H\"older-Brascamp-Lieb inequalities provide upper bounds for a class of multilinear expressions, in terms of $L^p$ norms of the functions involved. They have been extensively studied for functions defined on Euclidean spaces.…

Classical Analysis and ODEs · Mathematics 2015-10-15 Michael Christ , James Demmel , Nicholas Knight , Thomas Scanlon , Katherine Yelick

The high efficiency of a recently proposed method for computing with Gaussian processes relies on expanding a (translationally invariant) covariance kernel into complex exponentials, with frequencies lying on a Cartesian equispaced grid.…

Numerical Analysis · Mathematics 2023-05-19 Alex Barnett , Philip Greengard , Manas Rachh

Let $T$ be a multilinear operator which is bounded on certain products of unweighted Lebesgue spaces of $\mathbb R^n$. We assume that the associated kernel of $T$ satisfies some mild regularity condition which is weaker than the usual…

Classical Analysis and ODEs · Mathematics 2012-04-17 The Anh Bui , Xuan Thinh Duong

In this paper, we study a class of multilinear Gibbs measures with Hamiltonian given by a generalized $\mathrm{U}$-statistic and with a general base measure. Expressing the asymptotic free energy as an optimization problem over a space of…

Probability · Mathematics 2026-03-31 Sohom Bhattacharya , Nabarun Deb , Sumit Mukherjee

We investigate, under a volume constraint and among sets contained in a Euclidean half-space, the minimization problem of an energy functional given by the sum of a capillarity perimeter, a nonlocal interaction term and a gravitational…

Analysis of PDEs · Mathematics 2024-11-06 Giulio Pascale

Generalized Gibbs kernels are those that may take any direction not necessarily bounded to each axis along the parameters of the objective function. We study how to optimally choose such directions in a Directional, random scan, Gibbs…

We show that a positive operator between $L^p$-spaces is given by integration against a kernel function if and only if the image of each positive function has a lower semi-continuous representative with respect to a suitable topology. This…

Functional Analysis · Mathematics 2024-06-11 Moritz Gerlach , Jochen Glück

Consider the elliptic operator \[ A = - \sum_{k,l=1}^d \partial_k \, c_{kl} \, \partial_l + \sum_{k=1}^d a_k \, \partial_k - \sum_{k=1}^d \partial_k \, b_k + a_0 \] on a bounded connected open set $\Omega \subset {\bf R}^d$ with Lipschitz…

Analysis of PDEs · Mathematics 2019-10-17 A. F. M. ter Elst , M. F. Wong