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Given scalars $a_n (\neq 0)$ and $b_n$, $n \geq 0$, the tridiagonal kernel or band kernel with bandwidth $1$ is the positive definite kernel $k$ on the open unit disc $\mathbb{D}$ defined by \[ k(z, w) = \sum_{n=0}^\infty \Big((a_n + b_n…

Functional Analysis · Mathematics 2021-04-13 Susmita Das , Jaydeb Sarkar

In this paper we describe an intrinsically geometric way of producing magnetic fields on $\S^3$ and $\R^3$ for which the corresponding Dirac operators have a non-trivial kernel. In many cases we are able to compute the dimension of the…

Mathematical Physics · Physics 2015-06-26 Laszlo Erdos , Jan Philip Solovej

Kernel methods have been widely applied to machine learning and other questions of approximating an unknown function from its finite sample data. To ensure arbitrary accuracy of such approximation, various denseness conditions are imposed…

Machine Learning · Statistics 2013-10-25 Benxun Wang , Haizhang Zhang

We introduce a class of iterated logarithmic Lipschitz spaces $\mathcal{L}^{(k)}$, $k\in\mathbb{N}$, on an infinite tree which arise naturally in the context of operator theory. We characterize boundedness and compactness of the…

Functional Analysis · Mathematics 2022-07-26 Robert F. Allen , Flavia Colonna , Glenn R. Easley

We analyse the convergence of sampling algorithms for functions in reproducing kernel Hilbert spaces (RKHS). To this end, we discuss approximation properties of kernel regression under minimalistic assumptions on both the kernel and the…

Machine Learning · Statistics 2025-04-21 Armin Iske

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a…

Statistics Theory · Mathematics 2009-09-29 Thomas Hofmann , Bernhard Schölkopf , Alexander J. Smola

We provide necessary and sufficient conditions for a pair $S,T$ of Hilbert space operators in order that they satisfy $S^*=T$ and $T^*=S$. As a main result we establish an improvement of von Neumann's classical theorem on the positive…

Functional Analysis · Mathematics 2020-02-05 Zoltán Sebestyén , Zsigmond Tarcsay

Cauchy-de Branges spaces are Hilbert spaces of entire functions defined in terms of Cauchy transforms of discrete measures on the plane and generalizing the classical de Branges theory. We consider extensions of two important properties of…

Complex Variables · Mathematics 2022-06-07 Anton Baranov

In this paper we investigate the reproducing kernel Hilbert space where the polylogarithm appears as kernel functions. This investigation begins with the properties of functions in this space, and here a connection to the classical Hardy…

Functional Analysis · Mathematics 2015-03-06 Joel A. Rosenfeld

We review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors. The rate of contraction of…

Functional Analysis · Mathematics 2008-12-18 A. W. van der Vaart , J. H. van Zanten

The paper revisits concretely the algebraic K-theory in the light of the global program of Langlands by taking into account the new algebraic interpretation of homotopy viewed as deformation(s) of Galois representations given by…

General Mathematics · Mathematics 2010-09-15 Christian Pierre

In this paper we consider the reproducing kernel thesis for boundedness and compactness for various operators on Bergman-type spaces. In particular, the results in this paper apply to the weighted Bergman space on the unit ball, the unit…

Complex Variables · Mathematics 2018-02-09 Mishko Mitkovski , Brett D. Wick

We combine the coordinate method and Erlangen program in the framework of noncommutative geometry through an investigation of symmetries of noncommutative coordinate algebras. As the model we use the coherent states construction and the…

Mathematical Physics · Physics 2009-09-25 Vladimir V. Kisil

It is often said that control and estimation problems are in duality. Recently, in (Aubin-Frankowski,2021), we found new reproducing kernels in Linear-Quadratic optimal control by focusing on the Hilbert space of controlled trajectories,…

Optimization and Control · Mathematics 2022-10-14 Pierre-Cyril Aubin-Frankowski , Alain Bensoussan

We show, by means of a class of examples, that if $K_1$ and $K_2$ are two positive definite kernels on the unit disc such that the multiplication by the coordinate function on the corresponding reproducing kernel Hilbert space is subnormal,…

Functional Analysis · Mathematics 2017-05-30 Soumitra Ghara , Surjit Kumar

Kernel theorems, in general, provide a convenient representation of bounded linear operators. For the operator acting on a concrete function space, this means that its action on any element of the space can be expressed as a generalised…

Functional Analysis · Mathematics 2024-05-22 Dimitri Bytchenkoff , Michael Speckbacher , Peter Balazs

We apply the theory of de Branges-Rovnyak spaces to describe kernels of some Toeplitz operators on the classical Hardy space $H^2$. In particular, we discuss the kernels of the operators $T_{\bar f/ f}$ and $T_{\bar I\bar f/ f}$, where $f$…

Functional Analysis · Mathematics 2020-03-02 Maria T. Nowak , Paweł Sobolewski , Andrzej Sołtysiak , Magdalena Wołoszkiewicz-Cyll

We compute the genus one family Gromov-Witten invariants of K3 surfaces for non-primitive classes. These calculations verify Gottsche-Yau-Zaslow formula for non-primitive classes with index two. Our approach is to use the genus two…

Symplectic Geometry · Mathematics 2007-05-23 Junho Lee , Naichung Conan Leung

We study the spectral convergence of compact, self-adjoint operators on a separable Hilbert space under operator norm perturbations, and derive asymptotic expansions for their eigenvalues and eigenprojections. Our analysis focuses on…

Statistics Theory · Mathematics 2026-02-10 Eunseong Bae , Wolfgang Polonik

In this paper, we study the statistical and geometrical properties of the Kullback-Leibler divergence with kernel covariance operators (KKL) introduced by Bach [2022]. Unlike the classical Kullback-Leibler (KL) divergence that involves…

Machine Learning · Statistics 2025-03-12 Clémentine Chazal , Anna Korba , Francis Bach