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Related papers: Generalized de Branges-Rovnyak spaces

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We introduce the Yat kernel $$k_{b,\varepsilon}(\mathbf{w},\mathbf{x})=\frac{(\mathbf{w}^\top\mathbf{x}+b)^2}{\|\mathbf{x}-\mathbf{w}\|^2+\varepsilon},\qquad b\ge 0,\ \varepsilon>0,$$ a rational hidden-unit primitive whose units are Mercer…

Machine Learning · Computer Science 2026-05-06 Taha Bouhsine

We derive necessary density conditions for sampling and for interpolation in general reproducing kernel Hilbert spaces satisfying some natural conditions on the geometry of the space and the reproducing kernel. If the volume of shells is…

Functional Analysis · Mathematics 2018-04-03 Hartmut Führ , Karlheinz Gröchenig , Antti Haimi , Andreas Klotz , José Luis Romero

This article describes Hilbert spaces contractively contained in certain reproducing kernel Hilbert spaces of analytic functions on the open unit disc which are nearly invariant under division by an inner function. We extend Hitt's theorem…

Functional Analysis · Mathematics 2025-02-19 Arshad Khan , Sneh Lata , Dinesh Singh

This is a survey on reproducing kernel Krein spaces and their interplay with operator valued Hermitian kernels. Existence and uniqueness properties are carefully reviewed. The approach we follow in this survey uses a more abstract but very…

Functional Analysis · Mathematics 2025-11-04 Aurelian Gheondea

We introduce a complex-valued counterpart of the representer theorem in machine learning. We study several learning and minimization problems in reproducing kernel Hilbert spaces (RKHSs), with the aim of identifying appropriate input-output…

Functional Analysis · Mathematics 2026-04-28 Natanael Alpay , Antonino De Martino , Kamal Diki

In most classical holomorphic function spaces on the unit disk, a function $f$ can be approximated in the norm of the space by its dilates $f\_r(z):=f(rz)~(r \textless{} 1)$. We show that this is \emph{not} the case for the de…

Functional Analysis · Mathematics 2015-01-14 O. El-Fallah , E. Fricain , K. Kellay , J. Mashreghi , Ransford Tom

Let $\{v_n\}$ be a complete minimal system in a Hilbert space $\mathcal{H}$ and let $\{w_m\}$ be its biorthogonal system. It is well known that $\{w_m\}$ is not necessarily complete. However the situation may change if we consider systems…

Complex Variables · Mathematics 2011-12-26 Anton Baranov , Yurii Belov

We characterize the de Branges-Rovnyak spaces with complete Nevanlinna-Pick property. Our method relies on the general theory of reproducing kernel Hilbert spaces.

Functional Analysis · Mathematics 2020-03-12 Cheng Chu

This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may be viewed as a generalization of reproducing kernel Hilbert spaces (RKHSs). A key point is to endow Banach spaces with reproducing kernels such that…

Functional Analysis · Mathematics 2017-06-27 Yuesheng Xu , Qi Ye

A general theory of reproducing kernels and reproducing kernel Hilbert spaces on a right quaternionic Hilbert space is presented. Positive operator valued measures and their connection to a class of generalized quaternionic coherent states…

Mathematical Physics · Physics 2017-09-11 K. Thirulogasanthar , S. Twareque Ali

On a compact Lie group $G$, we consider the reproducing kernel Hilbert space $\mathcal{H}_K$ associated with the integral kernel $K$ of a left-invariant, positive, symmetric, trace class integral operator on $L^2(G)$. We present lower and…

Functional Analysis · Mathematics 2026-02-03 Zhirayr Avetisyan , Michael Ruzhansky , Karina Gonzalez

Reproducing kernel Hilbert spaces are uniquely characterized by their kernel, but reproducing kernel Banach spaces (RKBS) are not. However, a characterization of which RKBS admit a given kernel as reproducing kernel is lacking. This work…

Functional Analysis · Mathematics 2026-03-31 Tjeerd Jan Heeringa

In a recent paper we used a basic decomposition property of polyanalytic functions of order $2$ in one complex variable to characterize solutions of the classical $\overline{\partial}$-problem for given analytic and polyanalytic data. Our…

Complex Variables · Mathematics 2022-10-18 Daniel Alpay , Fabrizio Colombo , Kamal Diki , Irene Sabadini , Daniele C. Struppa

We introduce a vector differential operator $\mathbf{P}$ and a vector boundary operator $\mathbf{B}$ to derive a reproducing kernel along with its associated Hilbert space which is shown to be embedded in a classical Sobolev space. This…

Numerical Analysis · Mathematics 2011-09-28 Gregory E. Fasshauer , Qi Ye

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

Let $H_m(\mathbb B)$ be the analytic functional Hilbert space on the unit ball $\mathbb B \subset \mathbb C^n$ with reproducing kernel $K_m(z,w) = (1 - \langle z,w \rangle)^{-m}$. Using algebraic operator identities we characterize those…

Functional Analysis · Mathematics 2018-01-24 Jörg Eschmeier , Sebastian Langendörfer

Reproducing kernel Hilbert spaces (RKHSs) are Hilbert spaces of functions where pointwise evaluation is continuous. There are known examples of RKHSs that are Banach algebras under pointwise multiplication. These examples are built from…

Functional Analysis · Mathematics 2024-02-09 Dimitrios Giannakis , Michael Montgomery

We extend the de Branges-Rovnyak model for completely non-coisometric (CNC) linear contractions on a Hilbert space to the non-commutative multivariate setting of CNC row contractions. Namely, we show that any CNC contraction from several…

Functional Analysis · Mathematics 2026-01-09 Robert T. W. Martin , Jeet Sampat

The aim of the paper is to create a link between the theory of reproducing kernel Hilbert spaces (RKHS) and the notion of a unitary representation of a group or of a groupoid. More specifically, it is demonstrated on one hand, how to…

Functional Analysis · Mathematics 2021-02-22 Monika Drewnik , Tomasz Miller , Zbigniew Pasternak-Winiarski

Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selected based on characteristics of the problem and the data. This kernel is then employed to…

Machine Learning · Statistics 2024-03-12 Paul Dommel , Alois Pichler