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Algorithms and underlying mathematics are presented for numerical computation with periodic functions via approximations to machine precision by trigonometric polynomials, including the solution of linear and nonlinear periodic ordinary…

Numerical Analysis · Mathematics 2015-11-03 Grady B. Wright , Mohsin Javed , Hadrien Montanelli , Lloyd N. Trefethen

We describe those reproducing kernel Hilbert spaces of holomorphic functions on domains in ${\Bbb C}^d$ for which an analogue of the Nevanlinna-Pick theorem holds, in other words when the existence of a (possibly matrix-valued) function in…

Functional Analysis · Mathematics 2016-10-07 Jim Agler , John E. McCarthy

We study sufficient conditions on weight functions under which norm approximations by analytic polynomials are possible. The weights we study include radial, non-radial, and angular weights.

Functional Analysis · Mathematics 2022-02-09 Ali Abkar

In connection with the Herglotz-Nevanlinna integral representation of so-called Pick functions, we introduce the notion of boundary measure of holomorphic functions on the imaginary domain and elucidate some of basic properties.

Complex Variables · Mathematics 2025-05-22 Shigeru Yamagami

We study the $n$-point differentials corresponding to Kadomtsev-Petviashvili tau functions of hypergeometric type (also known as Orlov-Scherbin partition functions), with an emphasis on their $\hbar^2$-deformations and expansions. Under the…

Mathematical Physics · Physics 2024-06-12 Boris Bychkov , Petr Dunin-Barkowski , Maxim Kazarian , Sergey Shadrin

We prove a factorization theorem for reproducing kernel Hilbert spaces whose kernel has a normalized complete Nevanlinna-Pick factor. This result relates the functions in the original space to pointwise multipliers determined by the…

Functional Analysis · Mathematics 2018-07-19 Alexandru Aleman , Michael Hartz , John E. McCarthy , Stefan Richter

Recent years have witnessed the introduction and development of extremely fast rational function algorithms. Many ideas in this realm arose from polynomial-based linear-algebraic algorithms. However, polynomial approximation is occasionally…

Numerical Analysis · Mathematics 2025-10-03 James Chok , Geoffrey M. Vasil

The challenge of approximating functions in infinite-dimensional spaces from finite samples is widely regarded as formidable. We delve into the challenging problem of the numerical approximation of Sobolev-smooth functions defined on…

Optimization and Control · Mathematics 2024-10-11 Massimo Fornasier , Pascal Heid , Giacomo Enrico Sodini

Rational and neural network based approximations are efficient tools in modern approximation. These approaches are able to produce accurate approximations to nonsmooth and non-Lipschitz functions, including multivariate domain functions. In…

Optimization and Control · Mathematics 2023-09-08 Vinesha Peiris , Reinier Diaz Millan , Nadezda Sukhorukova , Julien Ugon

We prove the convergence case of Khintchine's theorem, with general approximation functions that are not necessarily monotonic, for analytic nonplanar manifolds over local fields of positive characteristic. Our approach is based on the…

Number Theory · Mathematics 2026-03-03 Noy Soffer Aranov , Sourav Das , Arijit Ganguly , Aratrika Pandey

We discuss the concept of inner function in reproducing kernel Hilbert spaces with an orthogonal basis of monomials and examine connections between inner functions and optimal polynomial approximants to $1/f$, where $f$ is a function in the…

Classical Analysis and ODEs · Mathematics 2019-08-15 Catherine Bénéteau , Matthew Fleeman , Dmitry Khavinson , Daniel Seco , Alan Sola

In this paper, we introduce a method known as polynomial frame approximation for approximating smooth, multivariate functions defined on irregular domains in $d$ dimensions, where $d$ can be arbitrary. This method is simple, and relies only…

Numerical Analysis · Mathematics 2020-05-27 Ben Adcock , Daan Huybrechs

The structural properties of fluids whose molecules interact via potentials with a hard-core plus n piece-wise constant sections of different widths and heights are derived using a (semi-analytical) rational-function approximation method.…

Soft Condensed Matter · Physics 2012-07-13 A. Santos , S. B. Yuste , M. López de Haro

We present an approximation theorem for continuous non-decreasing functions on compact preordered spaces, leading to an algebraic characterization of their corresponding function spaces. As an application, we prove that the family of…

Functional Analysis · Mathematics 2025-12-04 Ettore Minguzzi

Functions on a bounded domain in scientific computing are often approximated using piecewise polynomial approximations on meshes that adapt to the shape of the geometry. We study the problem of function approximation using splines on a…

Numerical Analysis · Mathematics 2020-08-27 Vincent Coppé , Daan Huybrechs

We discuss the relation between the linear Tschebyshev-Pad\'e approximations to analytic function $f$ and the diagonal type I Hermite-Pad\'e polynomials for the tuple of functions $[1,f_1,f_2]$ where the pair of functions $f_1,f_2$ forms…

Complex Variables · Mathematics 2021-06-07 Evguenii A. Rakhmanov , Sergey P. Suetin

For various Hilbert spaces of analytic functions on the unit disk, we characterize when a function $f$ has optimal polynomial approximants given by truncations of a single power series. We also introduce a generalized notion of optimal…

Functional Analysis · Mathematics 2023-07-11 Christopher Felder

We develop a version of Herbrand's theorem for continuous logic and use it to prove that definable functions in infinite-dimensional Hilbert spaces are piecewise approximable by affine functions. We obtain similar results for definable…

Logic · Mathematics 2011-07-20 Isaac Goldbring

The main purpose of the paper is to study sharp estimates of approximation of periodic functions in the H\"older spaces $H_p^{r,\alpha}$ for all $0<p\le\infty$ and $0<\alpha\le r$. By using modifications of the classical moduli of…

Classical Analysis and ODEs · Mathematics 2015-07-28 Yurii Kolomoitsev , Jürgen Prestin

We study approximations of the partition function of dense graphical models. Partition functions of graphical models play a fundamental role is statistical physics, in statistics and in machine learning. Two of the main methods for…

Machine Learning · Computer Science 2018-02-21 Vishesh Jain , Frederic Koehler , Elchanan Mossel