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We introduce a novel type of approximation spaces for functions with values in a nonlinear manifold. The discrete functions are constructed by piecewise polynomial interpolation in a Euclidean embedding space, and then projecting pointwise…

Numerical Analysis · Mathematics 2018-03-20 Philipp Grohs , Hanne Hardering , Oliver Sander , Markus Sprecher

We study the approximation of $L_p$-functions, $p\in (0,\infty]$, on cylindrical space-time domains $\Omega_T:=[0,T]\times \Omega$, $0<T<\infty$, $\Omega\subset \R^d$ Lipschitz, $d\in \mathbb{N}$, with respect to continuous anisotropic…

Numerical Analysis · Mathematics 2026-02-17 Pedro Morin , Cornelia Schneider , Nick Schneider

This paper develops a discontinuous Galerkin (DG) finite element differential calculus theory for approximating weak derivatives of Sobolev functions and piecewise Sobolev functions. By introducing numerical one-sided derivatives as…

Numerical Analysis · Mathematics 2013-03-06 Xiaobing Feng , Thomas Lewis , Michael Neilan

While the theory of operator approximation with any given accuracy is well elaborated, the theory of {best constrained} constructive operator approximation is still not so well developed. Despite increasing demands from applications this…

Optimization and Control · Mathematics 2018-11-09 Anatoli Torokhti , Pablo Soto-Quiros

We study variants of the mixed finite element method (mixed FEM) and the first-order system least-squares finite element (FOSLS) for the Poisson problem where we replace the load by a suitable regularization which permits to use $H^{-1}$…

Numerical Analysis · Mathematics 2023-02-03 Thomas Führer

The univariate spline quasi-interpolants (abbr. QIs) studied in this paper are approximation operators using B-spline expansions with coefficients which are linear combinations of discrete or weighted mean values of the function to be…

Numerical Analysis · Mathematics 2016-08-16 Domingo Barrera , Maria José Ibañez Pérez , Paul Sablonnière , D. Sbibih

We address the problem of approximating an unknown function from its discrete samples given at arbitrarily scattered sites. This problem is essential in numerical sciences, where modern applications also highlight the need for a solution to…

Numerical Analysis · Mathematics 2023-05-16 Nir Sharon , Rafael Sherbu Cohen , Holger Wendland

We give almost-linear-time algorithms for constructing sparsifiers with $n\ poly(\log n)$ edges that approximately preserve weighted $(\ell^{2}_2 + \ell^{p}_p)$ flow or voltage objectives on graphs. For flow objectives, this is the first…

Data Structures and Algorithms · Computer Science 2021-02-16 Deeksha Adil , Brian Bullins , Rasmus Kyng , Sushant Sachdeva

In this paper, we will discuss the notion of almost orthogonality in a functional sequence.Especially, we will define a few sequences of almost orthogonal polynomials which can be used successfully for modeling of electronic systems which…

Numerical Analysis · Mathematics 2010-07-22 Predrag Rajkovic , Sladjana Marinkovic

We outline the super-resolution reconstruction problem posed as a maximization of probability. We then introduce an interpolation method based on polygonal pixel overlap, express it as a linear operator, and use it to improve…

Computer Vision and Pattern Recognition · Computer Science 2012-10-17 Stéfan J. van der Walt , B. M. Herbst

The purpose of this paper is twofold. First, basic concepts such as Gamma function, almost convergence, fractional order difference operator and sequence spaces are given as a survey character. Thus, the current knowledge about those…

Functional Analysis · Mathematics 2016-10-04 Murat Kirisci , Ugur Kadak

This work introduces finite element methods for a class of elliptic fully nonlinear partial differential equations. They are based on a minimal residual principle that builds upon the Alexandrov--Bakelman--Pucci estimate. Under rather…

Numerical Analysis · Mathematics 2025-07-03 Dietmar Gallistl , Ngoc Tien Tran

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered…

Numerical Analysis · Mathematics 2024-03-12 Yifan Chen , Houman Owhadi , Florian Schäfer

In this paper, we study the approximation of an unknown quasiconcave function based on limited partial information. Available information includes lower bounds on the values of the target function at a specified set of points, as well as…

Risk Management · Quantitative Finance 2025-08-14 Jian Wu , William B. Haskell , Wenjie Huang , Huifu Xu

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

The paper aims at proposing an efficient and stable quasi-interpolation based method for numerically computing the Helmholtz-Hodge decomposition of a vector field. To this end, we first explicitly construct a matrix kernel in a general form…

Numerical Analysis · Mathematics 2024-12-09 Nicholas Fisher , Gregory Fasshauer , Wenwu Gao

In order to realize analog compressed sensing, the paper considers approximate proximal operators of the $\ell_1$ and minimax concave penalty (MCP) regularization functions. Specifically, we propose to realize the approximate functions by…

Information Theory · Computer Science 2025-10-15 Soma Furusawa , Taisei Kato , Ryo Hayakawa , Kazunori Hayashi

We introduce the concept of half-closed nodes for nodal discontinuous Galerkin (DG) discretisations. Unlike more commonly used closed nodes in DG, where on every element nodes are placed on all of its boundaries, half-closed nodes only…

Numerical Analysis · Mathematics 2024-11-21 Yulong Pan , Per-Olof Persson

In this paper, we present a new polygonal finite element method, called the Zipped Finite Element Method, for star-shaped polygons. The proposed approach constructs high-order shape functions as linear combinations of standard finite…

Numerical Analysis · Mathematics 2025-11-27 Stefano Berrone , Lorenzo Neva , Moreno Pintore , Gioana Teora , Fabio Vicini

We introduce new Gaussian Process (GP) high-order approximations to linear operations that are frequently used in various numerical methods. Our method employs the kernel-based GP regression modeling, a non-parametric Bayesian approach to…

Computational Physics · Physics 2025-06-09 Christopher DeGrendele , Dongwook Lee