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In this paper, we show that the approximation of high-dimensional functions, which are effectively low-dimensional, does not suffer from the curse of dimensionality. This is shown first in a general reproducing kernel Hilbert space set-up…

Numerical Analysis · Mathematics 2024-11-28 Christian Rieger , Holger Wendland

The starting point of this paper is the existence of Peano curves, that is, continuous surjections mapping the unit interval onto the unit square. From this fact one can easily construct of a continuous surjection from the real line…

General Topology · Mathematics 2015-10-01 N. Albuquerque , L. Bernal-Gonzalez , D. Pellegrino , J. B. Seoane-Sepulveda

Analogous to Weil-Petersson quasicircles, we investigate infinite circle patterns in the Euclidean plane parameterized by discrete harmonic functions of finite Dirichlet energy. The space of such circle patterns forms an…

Geometric Topology · Mathematics 2026-03-11 Wai Yeung Lam

Kernel methods in machine learning use a kernel function that takes two data points as input and returns their inner product after mapping them to a Hilbert space, implicitly and without actually computing the mapping. For many kernel…

Machine Learning · Computer Science 2024-10-17 Kamaledin Ghiasi-Shirazi , Mohammadreza Qaraei

Infinite-dimensional, holomorphic functions have been studied in detail over the last several decades, due to their relevance to parametric differential equations and computational uncertainty quantification. The approximation of such…

Numerical Analysis · Mathematics 2025-02-20 Ben Adcock , Nick Dexter , Sebastian Moraga

We describe Hilbert's spacefilling curve in several different ways: as an automatic sequence of directions,as a regular and synchronized sequence of coordinates of lattice points encountered, and as an automatic bitmap image.

Formal Languages and Automata Theory · Computer Science 2021-06-03 Jeffrey Shallit

We initiate a systematic study of intrinsic dimensional versions of classical functional inequalities which capture refined properties of the underlying objects. We focus on model spaces: Euclidean space, Hamming cube, and manifolds of…

Probability · Mathematics 2023-04-28 Alexandros Eskenazis , Yair Shenfeld

Functional data that are nonnegative and have a constrained integral can be considered as samples of one-dimensional density functions. Such data are ubiquitous. Due to the inherent constraints, densities do not live in a vector space and,…

Statistics Theory · Mathematics 2016-01-13 Alexander Petersen , Hans-Georg Müller

This paper demonstrates that the space of piecewise smooth functions can be well approximated by the space of functions defined by a set of simple (non-linear) operations on smooth uniform splines. The examples include bivariate functions…

Numerical Analysis · Mathematics 2024-05-13 David Levin

In the author's PhD thesis (2019) universal envelopes were introduced as a tool for studying the continuously obtainable information on discontinuous functions. To any function $f \colon X \to Y$ between $\operatorname{qcb}_0$-spaces one…

Logic in Computer Science · Computer Science 2023-06-22 Eike Neumann

Space filling curves are widely used in Computer Science. In particular Hilbert curves and their generalisations to higher dimension are used as an indexing method because of their nice locality properties. This article generalises this…

Data Structures and Algorithms · Computer Science 2019-04-17 Patrick Erik Bradley , Markus Wilhelm Jahn

We study numerical integration of functions depending on an infinite number of variables. We provide lower error bounds for general deterministic linear algorithms and provide matching upper error bounds with the help of suitable multilevel…

Numerical Analysis · Mathematics 2021-02-09 Josef Dick , Michael Gnewuch

We develop sampling formulas for high-dimensional functions in reproducing kernel Hilbert spaces, where we rely on irregular samples that are taken at determining sequences of data points. We place particular emphasis on sampling formulas…

Machine Learning · Computer Science 2025-04-21 Armin Iske , Lennart Ohlsen

In this paper we introduce reproducing kernel Hilbert spaces of polyanalytic functions of infinite order. First we study in details the counterpart of the Fock space and related results in this framework. In this case the kernel function is…

Complex Variables · Mathematics 2021-12-30 Daniel Alpay , Fabrizio Colombo , Kamal Diki , Irene Sabadini

Data depth functions are a generalization of one-dimensional order statistics and medians to real spaces of dimension greater than one; in particular, a data depth function quantifies the centrality of a point with respect to a data set or…

Statistics Theory · Mathematics 2016-05-17 Michael Burr , Robert Fabrizio

Suppose H is a space of functions on X. If H is a Hilbert space with reproducing kernel then that structure of H can be used to build distance functions on X. We describe some of those and their interpretations and interrelations. We also…

Complex Variables · Mathematics 2012-05-01 Nicola Arcozzi , Richard Rochberg , Eric T. Sawyer , Brett D. Wick

We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to…

Graphics · Computer Science 2020-09-15 Liang Zhou , Chris R. Johnson , Daniel Weiskopf

We prove various results in infinite-dimensional differential calculus which relate differentiability properties of functions and associated operator-valued functions (e.g., differentials). The results are applied in two areas: 1. in the…

Functional Analysis · Mathematics 2022-03-04 Helge Glockner

A well known result of H. Attouch states that the Mosco convergence of a sequence of proper convex lower semicontinuous functions defined on a Hilbert space is equivalent to the pointwise convergence of the associated Moreau envelopes. In…

Functional Analysis · Mathematics 2017-08-22 Miroslav Bačák , Martin Montag , Gabriele Steidl

The objective of this paper is to improve the customary definition of redundancy by providing quantitative measures in its place, which we coin upper and lower redundancies, that match better with an intuitive understanding of redundancy…

Functional Analysis · Mathematics 2009-11-19 Bernhard G. Bodmann , Peter G. Casazza , Gitta Kutyniok