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The convergence rates on polynomial interpolation in most cases are estimated by Lebesgue constants. These estimates may be overestimated for some special points of sets for functions of limited regularities. In this paper, by applying the…

Numerical Analysis · Mathematics 2015-06-19 Shuhuang Xiang

The multigrid algorithm is a multilevel approach to accelerate the numerical solution of discretized differential equations in physical problems involving long-range interactions. Multiresolution analysis of wavelet theory provides an…

Computational Physics · Physics 2007-05-23 D. Yesilleten , T. A. Arias

We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary pre-assigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted l^p-penalties on the…

Functional Analysis · Mathematics 2025-10-20 Ingrid Daubechies , Michel Defrise , Christine De Mol

Frequency estimation is a fundamental problem in many areas. The well-known A&M and its variant estimators have established an estimation framework by iteratively interpolating the discrete Fourier transform (DFT) coefficients. In general,…

Signal Processing · Electrical Eng. & Systems 2021-05-31 Kai Wu , J. Andrew Zhang , Xiaojing Huang , Y. Jay Guo

This paper focuses on scattered data fitting problems on spheres. We study the approximation performance of a class of weighted spectral filter algorithms, including Tikhonov regularization, Landaweber iteration, spectral cut-off, and…

Numerical Analysis · Mathematics 2024-10-24 Shao-Bo Lin

We present a new rational approximation algorithm based on the empirical interpolation method for interpolating a family of parametrized functions to rational polynomials with invariant poles, leading to efficient numerical algorithms for…

Numerical Analysis · Mathematics 2025-01-23 Aidi Li , Yuwen Li

We study the worst-case approximation of multivariate periodic functions from the weighted Korobov space $H_{d,\alpha,\gamma}$ with smoothness $\alpha>1/2$ in the Lebesgue norm $L_p([0,1]^d)$ for $1\le p\le\infty$. We analyze a \emph{median…

Numerical Analysis · Mathematics 2026-03-06 Zexin Pan , Mou Cai , Josef Dick , Takashi Goda , Peter Kritzer

Given an isotropic random vector $X$ with log-concave density in Euclidean space $\Real^n$, we study the concentration properties of $|X|$ on all scales, both above and below its expectation. We show in particular that: \[ \P(\abs{|X|…

Functional Analysis · Mathematics 2011-06-03 Olivier Guédon , Emanuel Milman

The constrained mock-Chebyshev least squares operator is a linear approximation operator based on an equispaced grid of points. Like other polynomial or rational approximation methods, it was recently introduced in order to defeat the Runge…

Numerical Analysis · Mathematics 2022-09-21 Francesco Dell'Accio , Federico Nudo

Approximate methods have been considered as a means to the evaluation of discrete transforms. In this work, we propose and analyze a class of integer transforms for the discrete Fourier, Hartley, and cosine transforms (DFT, DHT, and DCT),…

Signal Processing · Electrical Eng. & Systems 2020-07-07 R. J. Cintra

We propose and study a general quasi-interpolation framework for stochastic function approximation, which stems and draws motivation from convolution-type solutions for certain practical weighted variational problems. We obtain our…

Numerical Analysis · Mathematics 2025-12-24 Wenwu Gao , Le Hu , Xingping Sun , Xuan Zhou

We present a generalized formulation for reweighted least squares approximations. The goal of this article is twofold: firstly, to prove that the solution of such problem can be expressed as a convex combination of certain interpolants when…

We consider scattered data approximation on product regions of equal and different dimensionality. On each of these regions, we assume quasi-uniform but unstructured data sites and construct optimal sparse grids for scattered data…

Numerical Analysis · Mathematics 2026-04-24 Michael Griebel , Helmut Harbrecht , Michael Multerer

Multiresolution analyses based upon interpolets, interpolating scaling functions introduced by Deslauriers and Dubuc, are particularly well-suited to physical applications because they allow exact recovery of the multiresolution…

Materials Science · Physics 2009-10-31 Ross A. Lippert , T. A. Arias , Alan Edelman

We consider a family of Caffarelli-Kohn-Nirenberg interpolation inequalities and weighted logarithmic Hardy inequalities which have been obtained recently as a limit case of the first ones. We discuss the ranges of the parameters for which…

Analysis of PDEs · Mathematics 2012-12-06 Jean Dolbeault , Maria J. Esteban

For any two-dimensional nearest neighbor shift of finite type X and any integer n > 0, one can define the horizontal strip shift H_n(X) to be the set of configurations on Z x {1,...,n} which do not contain any forbidden transitions for X.…

Dynamical Systems · Mathematics 2010-11-10 Ronnie Pavlov

A method for 3D interpolation between hard spheres is described. The function to be interpolated could be the charge density between atoms in condensed matter. Its electrostatic potential is found analytically, and so are various integrals.…

Materials Science · Physics 2016-09-07 Yoshiro Nohara , O. K. Andersen

Let $\tau_k$ be the $k$-fold divisor function. By constructing an approximant of $\tau_k$, denoted as $\tau_k^*$, which is a normalized truncation of the $k$-fold divisor function, we prove that when $\exp\left(C\log^{1/2}X(\log\log…

Number Theory · Mathematics 2024-07-09 Mengdi Wang

We study an abstract family of asymptotically degenerating variational problems. Those are natural generalisations of families of problems emerging upon application of a rescaled Floquet-Bloch-Gelfand transform to resolvent problems for…

Analysis of PDEs · Mathematics 2025-08-27 Shane Cooper , Ilia Kamotski , Valery P. Smyshlyaev

We introduce kernel thinning, a new procedure for compressing a distribution $\mathbb{P}$ more effectively than i.i.d. sampling or standard thinning. Given a suitable reproducing kernel $\mathbf{k}_{\star}$ and $O(n^2)$ time, kernel…

Machine Learning · Statistics 2024-05-14 Raaz Dwivedi , Lester Mackey
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