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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

Since its introduction, the Discrete Variable Representation (DVR) basis set has become an invaluable representation of state vectors and Hermitian operators in non-relativistic quantum dynamics and spectroscopy calculations. On the other…

Computational Physics · Physics 2014-05-30 Hamse Mussa

Motivated by applications, we introduce a general and new framework for operator valued positive definite kernels. We further give applications both to operator theory and to stochastic processes. The first one yields several dilation…

Functional Analysis · Mathematics 2024-07-31 Palle E. T. Jorgensen , James Tian

The necessary and sufficient conditions for existence of a generalized representer theorem are presented for learning Hilbert space-valued functions. Representer theorems involving explicit basis functions and Reproducing Kernels are a…

Machine Learning · Computer Science 2018-09-21 Sanket Diwale , Colin Jones

This paper presents reproducing kernel Hilbert spaces method to obtain the numerical solution for partial differential equation constrained optimization problem.

Optimization and Control · Mathematics 2016-11-09 Majid Darehmiraki

The performance of adaptive estimators that employ embedding in reproducing kernel Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers. Parameter convergence and error approximation rates depend on where and…

Systems and Control · Electrical Eng. & Systems 2020-09-08 Sai Tej Paruchuri , Jia Guo , Andrew Kurdila

In this paper we consider the reproducing kernel thesis for boundedness and compactness for operators on $\ell^2$--valued Bergman-type spaces. This paper generalizes many well--known results about classical function spaces to their…

Classical Analysis and ODEs · Mathematics 2015-05-21 Robert Rahm

There has been growing recent interest in probabilistic interpretations of kernel-based methods as well as learning in Banach spaces. The absence of a useful Lebesgue measure on an infinite-dimensional reproducing kernel Hilbert space is a…

Machine Learning · Statistics 2014-03-14 Irina Holmes , Ambar Sengupta

We present a method to construct a chain of reproducing kernel Hilbert spaces controlled by a first-order system of differential equations from a given unimodular function satisfying several conditions. One of the applications of that…

Functional Analysis · Mathematics 2025-10-21 Masatoshi Suzuki

The purpose of this work is to explore the existence and properties of reproducing kernel Hilbert subspaces of $L^2(\C, \, d^2z/\pi)$ based on subsets of complex Hermite polynomials. The resulting coherent states (CS) form a family…

Mathematical Physics · Physics 2015-06-12 S. Twareque Ali , Fabio Bagarello , Jean Pierre Gazeau

This short technical report presents some learning theory results on vector-valued reproducing kernel Hilbert space (RKHS) regression, where the input space is allowed to be non-compact and the output space is a (possibly…

Machine Learning · Statistics 2022-02-17 Junhyunng Park , Krikamol Muandet

We consider piecewise linear interpolation from the perspective of kernel interpolation and quadrature. If the Sobolev space $W_2^1(0, 1)$ is equipped with a suitable inner product, its reproducing kernel is piecewise linear and gives rise…

Numerical Analysis · Mathematics 2026-03-03 Toni Karvonen , Gabriele Santin , Tizian Wenzel

In this paper, we discuss the convergence analysis of the conjugate gradient-based algorithm for the functional linear model in the reproducing kernel Hilbert space framework, utilizing early stopping results in regularization against…

Statistics Theory · Mathematics 2023-10-05 Naveen Gupta , S. Sivananthan , Bharath K. Sriperumbudur

Targeting at sparse learning, we construct Banach spaces B of functions on an input space X with the properties that (1) B possesses an l1 norm in the sense that it is isometrically isomorphic to the Banach space of integrable functions on…

Machine Learning · Statistics 2015-01-16 Guohui Song , Haizhang Zhang , Fred J. Hickernell

Recently, there has been emerging interest in constructing reproducing kernel Banach spaces (RKBS) for applied and theoretical purposes such as machine learning, sampling reconstruction, sparse approximation and functional analysis.…

Machine Learning · Computer Science 2021-12-09 Rongrong Lin , Haizhang Zhang , Jun Zhang

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 construct a canonical correspondence from a wide class of reproducing kernels on infinite-dimensional Hermitian vector bundles to linear connections on these bundles. The linear connection in question is obtained through a pull-back…

Representation Theory · Mathematics 2013-10-23 Daniel Beltita , José E. Galé

We classify, up to a natural equivalence relation, vector fields of the plane which belong to the kernel of a 1--form. This form can be closed, in which case the vector fields are integrable, or not, in which case the differential of the…

Dynamical Systems · Mathematics 2024-11-13 Stavros Anastassiou

Recent reverses for the discrete generalised triangle inequality and its continuous version for vector-valued integrals in Banach spaces are surveyed. New results are also obtained. Particular instances of interest in Hilbert spaces and for…

Classical Analysis and ODEs · Mathematics 2009-09-29 Sever Silvestru Dragomir

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