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For univariate polynomials over arbitrary field the degree gives an upper bound on the number of roots (factor theorem) and as a related result for any finite point-set one can construct a polynomial of degree equal to the cardinality…

Commutative Algebra · Mathematics 2026-05-19 Olav Geil

We investigate superconvergence properties of the spectral interpolation involving fractional derivatives. Our interest in this superconvergence problem is, in fact, twofold: when interpolating function values, we identify the points at…

Numerical Analysis · Mathematics 2015-03-25 Xuan Zhao , Zhimin Zhang

Supervised manifold learning methods learn data representations by preserving the geometric structure of data while enhancing the separation between data samples from different classes. In this work, we propose a theoretical study of…

Machine Learning · Computer Science 2018-01-08 Elif Vural , Christine Guillemot

We consider regular polynomial interpolation algorithms on recursively defined sets of interpolation points which approximate global solutions of arbitrary well-posed systems of linear partial differential equations. Convergence of the…

Numerical Analysis · Mathematics 2008-07-10 Joerg Kampen

The usual univariate interpolation problem of finding a monic polynomial f of degree n that interpolates n given values is well understood. This paper studies a variant where f is required to be composite, say, a composition of two…

Algebraic Geometry · Mathematics 2021-03-31 Joachim von zur Gathen , Guillermo Matera

The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years. Meanwhile, existing…

Computer Vision and Pattern Recognition · Computer Science 2018-05-29 Cem Ornek , Elif Vural

The probabilistic degree of a Boolean function $f:\{0,1\}^n\rightarrow \{0,1\}$ is defined to be the smallest $d$ such that there is a random polynomial $\mathbf{P}$ of degree at most $d$ that agrees with $f$ at each point with high…

Computational Complexity · Computer Science 2019-10-08 Srikanth Srinivasan , Utkarsh Tripathi , S. Venkitesh

We consider the task of recovering a Sobolev function on a connected compact Riemannian manifold $M$ when given a sample on a finite point set. We prove that the quality of the sample is given by the $L_\gamma(M)$-average of the geodesic…

Numerical Analysis · Mathematics 2024-09-23 David Krieg , Mathias Sonnleitner

We develop a general method to calculate entropy numbers of standard Sobolev's classes on an arbitrary compact homogeneous Riemannian manifold. Our method is essentially based on a detailed study of geometric characteristics of norms…

Functional Analysis · Mathematics 2015-04-27 A. Kushpel , J. Levesley

The first part of this paper complements previous results on characterization of polynomials of least deviation from zero in Sobolev $p$-norm ($1<p<\infty$) for the case $p=1$. Some relevant examples are indicated. The second part deals…

Complex Variables · Mathematics 2021-12-17 Abel Díaz-González , Héctor Pijeira-Cabrera , Javier Quintero-Roba

From a sufficiently large point sample lying on a compact Riemannian submanifold of Euclidean space, one can construct a simplicial complex which is homotopy-equivalent to that manifold with high confidence. We describe a corresponding…

Algebraic Topology · Mathematics 2013-10-15 Steve Ferry , Konstantin Mischaikow , Vidit Nanda

We present a computational method for reconstructing a vector field on a convex polytope $\mathcal{P} \subset \mathbb{R}^d$ of arbitrary dimension from discrete samples. We specifically address the scenario where the vector field is subject…

Dynamical Systems · Mathematics 2026-02-03 Junyan Chu , Shizuo Kaji

We show that a polynomial equation of degree less than 5 and with real parameters can be solved by regarding the variable in which the polynomial depends as a complex variable. For do it so, we only have to separate the real and imaginary…

General Mathematics · Mathematics 2012-01-05 Ricardo S. Vieira

Motivated by polynomial approximations of differential forms, we study analytical and numerical properties of a polynomial interpolation problem that relies on function averages over interval segments. The usage of segment data gives rise…

Numerical Analysis · Mathematics 2023-09-04 Ludovico Bruni Bruno , Wolfgang Erb

In this paper we propose a method for the approximation of high-dimensional functions over finite intervals with respect to complete orthonormal systems of polynomials. An important tool for this is the multivariate classical analysis of…

Numerical Analysis · Mathematics 2022-01-31 Daniel Potts , Michael Schmischke

The study of proper rational mappings between balls in complex Euclidean spaces naturally leads to the relationship between the degree and imbedding dimension of such a mapping. The special case for monomial mappings is equivalent to the…

Complex Variables · Mathematics 2008-01-16 John P. D'Angelo , Jiri Lebl , Han Peters

Differentiable real function reproducing primes up to a given number and having a differentiable inverse function is constructed. This inverse function is compared with the Riemann-Von Mangoldt exact expression for the number of primes not…

Number Theory · Mathematics 2007-05-23 Lumomir Alexandrov , D. B. Baranov , Plamen Yotov

The authors of ``A note on the complexity of a phaseless polynomial interpolation'' have shown that phaseless polynomial interpolation over $\mathbf{Q}$ is possible with $n+2$ points, where $n$ is the upper-bound on the degree of a…

Computational Complexity · Computer Science 2026-03-24 Michał R. Przybyłek , Paweł Siedlecki

We develop a constructive piecewise polynomial approximation theory in weighted Sobolev spaces with Muckenhoupt weights for any polynomial degree. The main ingredients to derive optimal error estimates for an averaged Taylor polynomial are…

Numerical Analysis · Mathematics 2014-11-27 Ricardo H. Nochetto , Enrique Otarola , Abner J. Salgado

Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes…

Computer Vision and Pattern Recognition · Computer Science 2016-04-20 Elif Vural , Christine Guillemot