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We study the metric entropy of the metric space $B_n$ of all n-dimensional Banach spaces (the so-called Banach-Mazur compactum) equipped with the Banach-Mazur (multiplicative) "distance" $d$. We are interested either in estimates…

Functional Analysis · Mathematics 2019-02-20 Gilles Pisier

The distinction between conditional, unconditional, and absolute convergence in infinite-dimensional spaces has fundamental implications for computational algorithms. While these concepts coincide in finite dimensions, the Dvoretzky-Rogers…

Computation and Language · Computer Science 2026-01-14 Przemysław Spyra

We give an upper bound for the difference of two solutions of Euler schemes approximating the Cauchy problem \[\begin{cases} \dot{u}(t) + Au(t) \ni f(t) \quad (t \in [0, T]), \\ u(0) = u^0, \end{cases}\] where $A \subseteq X \times X$ is a…

Analysis of PDEs · Mathematics 2024-08-27 Johann Beurich

Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finite-dimensional input space to…

Numerical Analysis · Mathematics 2022-06-23 Nicholas H. Nelsen , Andrew M. Stuart

This paper focuses on the stability of both local and global error bounds for a proper lower semicontinuous convex function defined on a Banach space. Without relying on any dual space information, we first provide precise estimates of…

Optimization and Control · Mathematics 2024-10-08 Zhou Wei , Michel Théra , Jen-Chih Yao

This article introduces an innovative mathematical framework designed to tackle non-linear convex variational problems in reflexive Banach spaces. Our approach employs a versatile technique that can handle a broad range of variational…

Numerical Analysis · Mathematics 2023-09-13 Pablo M. Berná , Antonio Falcó

Recently, versions of neural networks with infinite-dimensional affine operators inside the computational units (``neural operator'' networks) have been applied to learn solutions to differential equations. To enable practical computations,…

Functional Analysis · Mathematics 2026-02-03 Vinícius Luz Oliveira , Vladimir G. Pestov

We study an expansion method for high-dimensional parabolic PDEs which constructs accurate approximate solutions by decomposition into solutions to lower-dimensional PDEs, and which is particularly effective if there are a low number of…

Analysis of PDEs · Mathematics 2016-11-08 Christoph Reisinger , Rasmus Wissmann

Given a Banach space X and one of its compact sets F, we consider the problem of finding a good n dimensional space X_n \subset X which can be used to approximate the elements of F. The best possible error we can achieve for such an…

Functional Analysis · Mathematics 2012-04-12 Ronald DeVore , Guergana Petrova , Przemyslaw Wojtaszczyk

This paper is concerned with the computable error estimates for the eigenvalue problem which is solved by the general conforming finite element methods on the general meshes. Based on the computable error estimate, we can give an…

Numerical Analysis · Mathematics 2016-06-21 Hehu Xie , Meiling Yue , Ning Zhang

In order to solve an initial value problem by the variational iteration method, a sequence of functions is produced which converges to the solution under some suitable conditions. In the nonlinear case, after a few iterations the terms of…

Numerical Analysis · Mathematics 2016-06-23 Davod Khojasteh Salkuyeh , Ali Tavakoli

We develop and analyse an adaptive fully mixed finite element method for stationary generalized bioconvective flows, where the Navier--Stokes equations with concentration-dependent viscosity are coupled with a conservation law for swimming…

Numerical Analysis · Mathematics 2026-01-14 Eligio Colmenares , Ricardo Ruiz-Baier , Dalidet Sanhueza

This is an expository paper on approximating functions from general Hilbert or Banach spaces in the worst case, average case and randomized settings with error measured in the $L_p$ sense. We define the power function as the ratio between…

Numerical Analysis · Mathematics 2011-06-08 Erich Novak , Henryk Woźniakowski

We consider the classical Cauchy problem for the linear heat equation and integrable initial data in the Euclidean space $\mathbb{R}^N$. In the case $N=1$ we show that given a weighted $L^p$-space $L_w^p(\mathbb{R})$ with $1 \leq p <…

Functional Analysis · Mathematics 2018-02-07 José Bonet , Wolfgang Lusky , Jari Taskinen

We introduce and analyze a fast iterative method based on sequential Bregman projections for nonlinear inverse problems in Banach spaces. The key idea, in contrast to the standard Landweber method, is to use multiple search directions per…

Numerical Analysis · Mathematics 2018-08-01 Anne Wald

Second order Sobolev metrics on the space of regular unparametrized planar curves have several desirable completeness properties not present in lower order metrics, but numerics are still largely missing. In this paper, we present…

Differential Geometry · Mathematics 2016-09-08 Martin Bauer , Martins Bruveris , Philipp Harms , Jakob Møller-Andersen

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 define the frame potential for a Schauder frame on a finite dimensional Banach space as the square of the $2$-summing norm of the frame operator. As is the case for frames for Hilbert spaces, we prove that the frame potential can be used…

Functional Analysis · Mathematics 2018-04-12 J. A. Chávez-Domínguez , D. Freeman , K. Kornelson

We construct an explicit solution of the Cauchy initial value problem for the n-dimensional Schroedinger equation with certain time-dependent Hamiltonian operator of a modified oscillator. The dynamical SU(1,1) symmetry of the harmonic…

Mathematical Physics · Physics 2009-11-13 Maria Meiler , Ricardo Cordero-Soto , Sergei K. Suslov

Modern large-scale statistical models require to estimate thousands to millions of parameters. This is often accomplished by iterative algorithms such as gradient descent, projected gradient descent or their accelerated versions. What are…

Machine Learning · Statistics 2020-03-04 Michael Celentano , Andrea Montanari , Yuchen Wu
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