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Related papers: The Bergman kernel in constant curvature

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We give upper bounds for the Bergman kernels associated to tensor powers of a smooth positive line bundle in terms of the rate of growth of the Taylor coefficients of the K\"ahler potential. As applications, we obtain improved off-diagonal…

Complex Variables · Mathematics 2018-07-03 Hamid Hezari , Hang Xu

We study the consistency of minimum-norm interpolation in reproducing kernel Hilbert spaces corresponding to bounded kernels. Our main result give lower bounds for the generalization error of the kernel interpolation measured in a…

Machine Learning · Statistics 2025-09-30 Yunfei Yang

In this paper, the concept of weakly uniform perfectness is considered. As an analogue of the theory of uniform perfectness, we obtain the relationships between weakly uniform perfectness and Bergman kernel, Poincar\'e metric and Hausdorff…

Complex Variables · Mathematics 2025-09-01 Zhiyuan Zheng

We compute the leading and sub-leading terms in the asymptotic expansion of the Szeg\"o kernel on the diagonal of a class of pseudoconvex Reinhardt domains whose boundaries are endowed with a general class of smooth measures. We do so by…

Complex Variables · Mathematics 2014-02-25 Arash Karami , Vamsi Pingali

We study diagonal estimates for the Bergman kernels of certain model domains in $\mathbb{C}^2$ near boundary points that are of infinite type. To do so, we need a mild structural condition on the defining functions of interest that…

Complex Variables · Mathematics 2011-05-18 Gautam Bharali

Quadrature formulas for spheres, the rotation group, and other compact, homogeneous manifolds are important in a number of applications and have been the subject of recent research. The main purpose of this paper is to study coordinate…

Numerical Analysis · Mathematics 2012-11-26 E. Fuselier , T. Hangelbroek , F. J. Narcowich , J. D. Ward , G. B. Wright

While direct statements for kernel based interpolation on regions $\Omega \subset \mathbb{R}^d$ are well researched, far less is known about corresponding inverse statements. The available inverse statements for kernel based interpolation…

Numerical Analysis · Mathematics 2025-04-23 Tizian Wenzel

For~weights $\rho$ which are either radial on the unit ball or depend only on the vertical coordinate on the upper half-space, we describe the asymptotic behaviour of the corresponding weighted harmonic Bergman kernels with respect to…

Complex Variables · Mathematics 2016-01-15 Miroslav Engliš

A weighted area estimate for entire graphs with bounded weighted mean curvature in Gauss space is given by a simple proof. Bernstein type theorems for self shrinkers (\cite {wa}) as well as for graphic $\lambda$-hypersurfaces (\cite{…

Differential Geometry · Mathematics 2018-03-02 Doan The Hieu

We obtain a quantitative estimate of Bergman distance when $\Omega \subset \mathbb{C}^n$ is a bounded domain with log-hyperconvexity index $\alpha_l(\Omega)>\frac{n-1+\sqrt{(n-1)(n+3)}}{2}$, as well as the $A^2(\log A)^q$-integrability of…

Complex Variables · Mathematics 2022-09-23 Bo-Yong Chen , Zhiyuan Zheng

Kernel mean embeddings -- integrals of a kernel with respect to a probability distribution -- are essential in Bayesian quadrature, but also widely used in other computational tools for numerical integration or for statistical inference…

Machine Learning · Statistics 2025-04-29 François-Xavier Briol , Alexandra Gessner , Toni Karvonen , Maren Mahsereci

We consider the geometry relaxation of an isolated point defect embedded in a homogeneous crystalline solid, within an atomistic description. We prove a sharp convergence rate for a periodic supercell approximation with respect to uniform…

Numerical Analysis · Mathematics 2018-11-22 Julian Braun , Christoph Ortner

We prove a folklore conjecture that the Bergman measure along a holomorphic family of curves parametrized by the punctured unit disk converges to the Zhang measure on the associated Berkovich space. The convergence takes place on a…

Algebraic Geometry · Mathematics 2020-05-13 Sanal Shivaprasad

We show how to compute the Bergman kernel functions of some special domains in a simple way. As an application of the explicit formulas, we show that the Bergman kernel functions of some convex domains, for instance the domain in C^3…

Complex Variables · Mathematics 2009-09-25 Harold P. Boas , Siqi Fu , Emil J. Straube

Non-Hermitian random matrices with symplectic symmetry provide examples for Pfaffian point processes in the complex plane. These point processes are characterised by a matrix valued kernel of skew-orthogonal polynomials. We develop their…

Mathematical Physics · Physics 2022-01-19 Gernot Akemann , Markus Ebke , Iván Parra

Let $X$ be a compact connected Riemann surface of genus $g$, with $g \geq 2$. For each $d <\eta(X)$, where $\eta(X)$ is the gonality of $X$, the symmetric product $\text{Sym}^d(X)$ embeds into $\text{Pic}^d(X)$ by sending an effective…

Differential Geometry · Mathematics 2016-09-21 Anilatmaja Aryasomayajula , Indranil Biswas , Archana S. Morye , Tathagata Sengupta

We consider mixed normed Bergman spaces on homogeneous Siegel domains. In the literature, two different approaches have been considered and several results seem difficult to be compared. In this paper we compare the results available in the…

Complex Variables · Mathematics 2023-11-13 Mattia Calzi , Marco M. Peloso

We consider an asymptotically flat Riemannian spin manifold of positive scalar curvature. An inequality is derived which bounds the Riemann tensor in terms of the total mass and quantifies in which sense curvature must become small when the…

Differential Geometry · Mathematics 2007-06-13 Felix Finster , Ines Kath

We prove a general version of the homological perturbation lemma which works in the presence of curvature, and without the restriction to strong deformation retracts, building on work of Markl. A key observation is that the notion of strong…

Algebraic Topology · Mathematics 2020-02-05 Matthew Hogancamp

Positive definite kernels and their associated Reproducing Kernel Hilbert Spaces provide a mathematically compelling and practically competitive framework for learning from data. In this paper we take the approximation theory point of view…

Machine Learning · Computer Science 2018-08-06 Mikhail Belkin
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