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In this paper, we examine the structure of systems that are weighted homogeneous for several systems of weights, and how it impacts the computation of Gr\"obner bases. We present several linear algebra algorithms for computing Gr\"obner…

Symbolic Computation · Computer Science 2024-04-09 Thibaut Verron

Discrete spectral transformations of skew orthogonal polynomials are presented. From these spectral transformations, it is shown that the corresponding discrete integrable systems are derived both in 1+1 dimension and in 2+1 dimension.…

Mathematical Physics · Physics 2012-03-01 Hiroshi Miki , Hiroaki Goda , Satoshi Tsujimoto

In this paper, we study a newly developed shearlet system on bounded domains which yields frames for $H^s(\Omega)$ for some $s\in \mathbb{N}$, $\Omega \subset \mathbb{R}^2$. We will derive approximation rates with respect to $H^s(\Omega)$…

Functional Analysis · Mathematics 2019-03-04 Philipp Petersen , Mones Raslan

The main purpose of this paper is to obtain an explicit expression of a family of matrix valued orthogonal polynomials {P_n}_n, with respect to a weight W, that are eigenfunctions of a second order differential operator D. The weight W and…

Representation Theory · Mathematics 2007-05-23 I. Pacharoni , P. Roman

In this paper we consider second order parabolic partial differential equations subject to the Dirichlet boundary condition on smooth domains. We establish weighted $L_{q}$-maximal regularity in weighted Triebel-Lizorkin spaces for such…

Analysis of PDEs · Mathematics 2019-11-07 Nick Lindemulder

In this contribution we deal with Gaussian quadrature rules based on orthogonal polynomials associated with a weight function $w(x)= x^{\alpha} e^{-x}$ supported on an interval $(0,z)$, $z>0.$ The modified Chebyshev algorithm is used in…

Numerical Analysis · Mathematics 2024-01-05 Juan C. García-Ardila , Francisco Marcellán

We study orthogonal polynomials for a weight function defined over a domain of revolution, where the domain is formed from rotating a two-dimensional region and goes beyond the quadratic domains. Explicit constructions of orthogonal bases…

Classical Analysis and ODEs · Mathematics 2023-11-28 Yuan Xu

This is the second part of a series of four articles on weighted norm inequalities, off-diagonal estimates and elliptic operators. We consider a substitute to the notion of pointwise bounds for kernels of operators which usually is a…

Classical Analysis and ODEs · Mathematics 2018-10-10 Pascal Auscher , José Maria Martell

Enforcing orthonormal or isometric property for the weight matrices has been shown to enhance the training of deep neural networks by mitigating gradient exploding/vanishing and increasing the robustness of the learned networks. However,…

Machine Learning · Computer Science 2024-03-01 Zhen Qin , Xuwei Tan , Zhihui Zhu

We derive upper bounds on the difference between the orthogonal projections of a smooth function $u$ onto two finite element spaces that are nearby, in the sense that the support of every shape function belonging to one but not both of the…

Numerical Analysis · Mathematics 2014-08-19 Evan S. Gawlik , Adrian J. Lew

We consider the homogeneous equation ${\mathcal A} u=0$, where ${\mathcal A}$ is a symmetric and coercive elliptic operator in $H^1(\Omega)$ with $\Omega$ bounded domain in ${{\mathbb R}}^d$. The boundary conditions involve fractional power…

Numerical Analysis · Mathematics 2017-02-22 Raytcho Lazarov , Petr Vabishchevich

We study the convergence to equilibrium in high dimensions, focusing on explicit bounds on mixing times and the emergence of the cutoff phenomenon for Dyson-Laguerre processes. These are interacting particle systems with non-constant…

Probability · Mathematics 2025-09-25 Samuel Chan-Ashing

We study a class of weight functions on $[-1,1]$, which are special cases of the general weights studied by Bernstein and Szeg\"o, as well as their extentions to the interval $[-a,1]$ for a continuous parameter $a>0$. These weights are…

Classical Analysis and ODEs · Mathematics 2025-09-16 Martin Nicholson

In this paper, we study the spectrality and frame-spectrality of exponential systems of the type $E(\Lambda,\varphi) = \{e^{2\pi i \lambda\cdot\varphi(x)}: \lambda\in\Lambda\}$ where the phase function $\varphi$ is a Borel measurable which…

Functional Analysis · Mathematics 2020-07-09 Jean-Pierre Gabardo , Chun-Kit Lai , Vignon Oussa

Based on the Wronski determinant, we propose the construction of linearly independent and orthogonal functions in any Hilbert function space. The method requires only an initial function from the space of functions under consideration, that…

Functional Analysis · Mathematics 2026-05-19 Athanasios Christou Micheas

The spectral properties of a class of non-selfadjoint second order elliptic operators with indefinite weight functions on unbounded domains $\Omega$ are investigated. It is shown that under an abstract regularity assumption the nonreal…

Spectral Theory · Mathematics 2015-11-10 Jussi Behrndt

In this paper, we establish some Strichartz estimates for orthonormal functions and probabilistic convergence of density functions related to compact operators on manifolds. Firstly, we present the suitable bound of $\int_{a\leq|s|\leq…

Probability · Mathematics 2024-11-12 Wei Yan , Jinqiao Duan , Jianhua Huang , Haoyuan Xu , Meihua Yang

We consider existence and uniqueness of symmetric approximation of frames by normalized tight frames and of symmetric orthogonalization of bases by orthonormal bases in Hilbert spaces H . More precisely, we determine whether a given frame…

Functional Analysis · Mathematics 2025-05-08 M. Frank , V. I. Paulsen , T. R. Tiballi

We present two new classes of orthogonal functions, log orthogonal functions (LOFs) and generalized log orthogonal functions (GLOFs), which are constructed by applying a $\log$ mapping to Laguerre polynomials. We develop basic approximation…

Numerical Analysis · Mathematics 2020-03-04 Sheng Chen , Jie Shen

We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error.…

Machine Learning · Computer Science 2016-10-31 Felix X. Yu , Ananda Theertha Suresh , Krzysztof Choromanski , Daniel Holtmann-Rice , Sanjiv Kumar
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