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In this paper we present a formula for Macdonald's polynomials for the root system A(n-1) which arises from the representation theory of quantum sl(n). This formula expresses Macdonald's polynomials via (weighted) traces of intertwining…

High Energy Physics - Theory · Physics 2008-02-03 Pavel Etingof , Alexander Kirillov

The spherical Radon-Dunkl transform $R_\kappa$, associated to weight functions invariant under a finite reflection group, is introduced, and some elementary properties are obtained in terms of $h$-harmonics. Several inversion formulas of…

Classical Analysis and ODEs · Mathematics 2009-03-04 Zhongkai Li , Futao Song

This work is concerned with the kernel-based approximation of a complex-valued function from data, where the frequency response function of a partial differential equation in the frequency domain is of particular interest. In this setting,…

Computational Engineering, Finance, and Science · Computer Science 2024-11-26 Julien Bect , Niklas Georg , Ulrich Römer , Sebastian Schöps

For any module over the affine Weyl group we construct a representation of the associated trigonometric Cherednik algebra $A(k)$ at critical level in terms of Dunkl type operators. Under this representation the center of $A(k)$ produces…

Representation Theory · Mathematics 2009-06-03 E. Emsiz , E. M. Opdam , J. V. Stokman

The kernel of the $\mathbb{Z}_2^{n}$ Dirac-Dunkl operator is examined. The symmetry algebra $\mathcal{A}_{n}$ of the associated Dirac-Dunkl equation on $\mathbb{S}^{n-1}$ is determined and is seen to correspond to a higher rank…

Mathematical Physics · Physics 2017-02-15 Hendrik De Bie , Vincent X. Genest , Luc Vinet

The rational Dunkl operators are commuting differential-reflection operators on the Euclidean space $\RR^d$ associated with a root system. The aim of the paper is to study local boundary behaviour of generalized harmonic functions…

Functional Analysis · Mathematics 2022-06-07 Jiaxi Jiu , Zhongkai Li

In this note, we demonstrate a method to invert some Hankel matrices explicitly by using the kernel polynomials for the related classical orthogonal polynomials.

Classical Analysis and ODEs · Mathematics 2009-03-24 Ruiming Zhang

This paper proposes a novel kernel approach to linear dimension reduction for supervised learning. The purpose of the dimension reduction is to find directions in the input space to explain the output as effectively as possible. The…

Machine Learning · Statistics 2011-09-05 Kenji Fukumizu , Chenlei Leng

In this article, we study some properties of the $n$-th order weighted reduced Bergman kernels for planar domains, $n\geq 1$. Specifically, we look at Ramadanov type theorems, localization, and boundary behaviour of the weighted reduced…

Complex Variables · Mathematics 2023-09-13 Sahil Gehlawat , Aakanksha Jain , Amar Deep Sarkar

Kernel-based approach to operator approximation for partial differential equations has been shown to be unconditionally stable for linear PDEs and numerically exhibit unconditional stability for non-linear PDEs. These methods have the same…

Numerical Analysis · Mathematics 2025-11-25 Andrew Christlieb , Sining Gong , Hyoseon Yang

We introduce a data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method rests on the assumption that the nonlinear system behaves…

Optimization and Control · Mathematics 2016-04-04 Jake Bouvrie , Boumediene Hamzi

We are concerned with the problem of recovering the radial kernel $k$, depending also on time, in a parabolic integro-differential equation $$D_{t}u(t,x)={\cal A}u(t,x)+\int_0^t k(t-s,|x|){\cal B}u(s,x)ds +\int_0^t D_{|x|}k(t-s,|x|){\cal…

Analysis of PDEs · Mathematics 2007-05-23 A. Favaron , A. Lorenzi

In this paper we characterise the optimal pointwise size and regularity estimates for the Dunkl Riesz transform kernel involving both the Euclidean metric and the Dunkl metric, where these two metrics are not equivalent. We further…

Classical Analysis and ODEs · Mathematics 2024-02-06 Yongsheng Han , Ming-Yi Lee , Ji Li , Brett D. Wick

High-dimensional data in the form of tensors are challenging for kernel classification methods. To both reduce the computational complexity and extract informative features, kernels based on low-rank tensor decompositions have been…

Machine Learning · Statistics 2023-02-17 Kirandeep Kour , Sergey Dolgov , Peter Benner , Martin Stoll , Max Pfeffer

Root subgroup factorization is a refinement of triangular (or LDU) factorization. For a complex reductive Lie group, and a choice of reduced factorization of the longest Weyl group element, the forward map from root subgroup coordinates to…

Group Theory · Mathematics 2018-12-20 Doug Pickrell

We consider diffusive systems, regarded as input/output systems with a kernel given as the Fourier--Borel transform of a measure in the left half-plane. Associated with these are a family of weighted Hankel integral operators, and we…

Functional Analysis · Mathematics 2017-04-04 Aolo Bashar Abusaksaka , Jonathan R. Partington

For loop integrals, the standard method is reduction. A well-known reduction method for one-loop integrals is the Passarino-Veltman reduction. Inspired by the recent paper [1] where the tadpole reduction coefficients have been solved, in…

High Energy Physics - Phenomenology · Physics 2022-01-05 Chang Hu , Tingfei Li , Xiaodi Li

On $\mathbb R^N$ equipped with a normalized root system $R$, a multiplicity function $k(\alpha) > 0$, and the associated measure $$ dw(\mathbf x)=\prod_{\alpha\in R}|\langle \mathbf x,\alpha\rangle|^{k(\alpha)}\, d\mathbf x, $$ let…

Functional Analysis · Mathematics 2021-11-30 Jacek Dziubański , Agnieszka Hejna

We give the Jordan form and the Singular Value Decomposition for an integral operator ${\cal N}$ with a non-symmetric kernel $N(y,z)$. This is used to give solutions of Fredholm equations for non-symmetric kernels, and to determine the…

Spectral Theory · Mathematics 2008-04-02 Christopher S. Withers , Saralees Nadarajah

Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm based on low rank constraint optimization and kernel-based…

Machine Learning · Computer Science 2020-02-23 Patrick Heas , Cedric Herzet , Benoit Combes