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Many problems of theoretical and practical interest involve finding an optimum over a family of convex functions. For instance, finding the projection on the convex functions in $H^k(\Omega)$, and optimizing functionals arising from some…

Numerical Analysis · Mathematics 2008-04-11 Néstor E. Aguilera , Pedro Morin

Recently the author and U. Reif introduced the concept of diversification of uniform tensor product B-splines. Based on this concept, we give a new constructive modification of non-uniform B-splines. The resulting spline spaces are…

Classical Analysis and ODEs · Mathematics 2016-11-17 Nada Sissouno

The motivation of this work stems from the numerical approximation of bounded functions by polynomials satisfying the same bounds. The present contribution makes use of the recent algebraic characterization found in [B. Despr\'es, Numer.…

Numerical Analysis · Mathematics 2020-06-30 Martin Campos Pinto , Frédérique Charles , Bruno Després , Maxime Herda

A new concept for the higher-order accurate approximation of partial differential equations on manifolds is proposed where a surface mesh composed by higher-order elements is automatically generated based on level-set data. Thereby, it…

Numerical Analysis · Computer Science 2017-10-11 T. P. Fries , D. Schöllhammer

Multilinear embedding estimates for the fractional Laplacian are obtained in terms of functionals defined over a hyperbolic surface. Convolution estimates used in the proof enlarge the classical framework of the convolution algebra for…

Analysis of PDEs · Mathematics 2012-04-26 William Beckner

We consider the regression problem of estimating functions on $\mathbb{R}^D$ but supported on a $d$-dimensional manifold $ \mathcal{M} \subset \mathbb{R}^D $ with $ d \ll D $. Drawing ideas from multi-resolution analysis and nonlinear…

Machine Learning · Statistics 2021-01-14 Wenjing Liao , Mauro Maggioni , Stefano Vigogna

We construct a family of non-parametric (infinite-dimensional) manifolds of finite measures on $R^d$. The manifolds are modelled on a variety of weighted Sobolev spaces, including Hilbert-Sobolev spaces and mixed-norm spaces. Each supports…

Probability · Mathematics 2023-05-26 Nigel J. Newton

In this paper we focus on the linear functionals defining an approximate version of the gradient of a function. These functionals are often used when dealing with optimization problems where the computation of the gradient of the objective…

Optimization and Control · Mathematics 2021-05-21 Marco Boresta , Tommaso Colombo , Alberto De Santis , Stefano Lucidi

This paper is devoted to rigidity results for some elliptic PDEs and related interpolation inequalities of Sobolev type on smooth compact connected Riemannian manifolds without boundaries. Rigidity means that the PDE has no other solution…

Analysis of PDEs · Mathematics 2014-05-02 Jean Dolbeault , Maria J. Esteban , Michael Loss

We demonstrate the equivalence of two classes of $D$-invariant polynomial subspaces introduced in [8] and [9], i.e., these two classes of subspaces are different representations of the breadth-one $D$-invariant subspace. Moreover, we solve…

Numerical Analysis · Mathematics 2014-07-29 Xue Jiang , Shugong Zhang

We present an algorithm for approximating a function defined over a $d$-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require any…

Machine Learning · Statistics 2020-08-13 Barak Sober , Yariv Aizenbud , David Levin

We study in this paper the function approximation error of multivariate linear extrapolation. The sharp error bound of linear interpolation already exists in the literature. However, linear extrapolation is used far more often in…

Optimization and Control · Mathematics 2026-05-20 Liyuan Cao , Zaiwen Wen , Ya-xiang Yuan

The existing approaches to intrinsic dimension estimation usually are not reliable when the data are nonlinearly embedded in the high dimensional space. In this work, we show that the explicit accounting to geometric properties of unknown…

Machine Learning · Statistics 2019-04-15 Marina Gomtsyan , Nikita Mokrov , Maxim Panov , Yury Yanovich

In this paper we get error bounds for fully discrete approximations of infinite horizon problems via the dynamic programming approach. It is well known that considering a time discretization with a positive step size $h$ an error bound of…

Numerical Analysis · Mathematics 2026-02-09 Javier de Frutos , Julia Novo

We study universal approximation of continuous functionals on compact subsets of products of Hilbert spaces. We prove that any such functional can be uniformly approximated by models that first take finitely many continuous linear…

Machine Learning · Computer Science 2026-02-04 Andrey Krylov , Maksim Penkin

We derive a priori error estimates for Nitsche's method applied to elliptic problems on approximate domains. Such approximations arise, for example, in unfitted finite element methods, data-driven simulations, and evolving domain problems,…

Numerical Analysis · Mathematics 2026-04-02 Mats G. Larson , Karl Larsson , Shantiram Mahata

A basic building block in Classical Potential Theory is the fundamental solution of the Laplace equation in ${\mathbb R}^d$ (Newtonian kernel). The main goal of this article is to study the rates of nonlinear $n$-term approximation of…

Classical Analysis and ODEs · Mathematics 2018-08-28 Kamen Ivanov , Pencho Petrushev

We propose an extrapolation technique that allows accuracy improvement of the discrete dipole approximation computations. The performance of this technique was studied empirically based on extensive simulations for 5 test cases using many…

Optics · Physics 2008-07-29 Maxim A. Yurkin , Valeri P. Maltsev , Alfons G. Hoekstra

The problem of finding global minima of nonlinear discrete functions arises in many fields of practical matters. In recent years, methods based on discrete filled functions become popular as ways of solving these sort of problems. However,…

Optimization and Control · Mathematics 2020-03-26 Juan Di Mauro , Hugo D. Scolnik

Since the 1960's the finite element method emerged as a powerful tool for the numerical simulation of countless physical phenomena or processes in applied sciences. One of the reasons for this undeniable success is the great versatility of…

Numerical Analysis · Mathematics 2018-12-05 Vitoriano Ruas