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Related papers: Chebyshev approximation by non-Chebyshev systems

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In the present work we consider rational best approximations to the exponential function that minimize a uniform error on a subset of the imaginary axis. Namely, Chebyshev approximation and unitary best approximation where the latter is…

Numerical Analysis · Mathematics 2024-10-10 Tobias Jawecki

The problem of low rank approximation is ubiquitous in science. Traditionally this problem is solved in unitary invariant norms such as Frobenius or spectral norm due to existence of efficient methods for building approximations. However,…

Numerical Analysis · Mathematics 2023-08-25 Stanislav Morozov , Matvey Smirnov , Nikolai Zamarashkin

The aim of the present work is to introduce a method based on Chebyshev polynomials for the numerical solution of a system of Cauchy type singular integral equations of the first kind on a finite segment. Moreover, an estimation error is…

Numerical Analysis · Mathematics 2015-08-11 Sedaghat Shahmorad , Samad Ahdiaghdam

We employ the generalized Remez algorithm, initially suggested by P. T. P. Tang, to perform an experimental study of Chebyshev polynomials in the complex plane. Our focus lies particularly on the examination of their norms and zeros. What…

Complex Variables · Mathematics 2025-07-11 Lennart Aljoscha Hübner , Olof Rubin

This paper considers an optimization problem for a dynamical system whose evolution depends on a collection of binary decision variables. We develop scalable approximation algorithms with provable suboptimality bounds to provide…

Optimization and Control · Mathematics 2016-10-31 Insoon Yang , Samuel A. Burden , Ram Rajagopal , S. Shankar Sastry , Claire J. Tomlin

We propose a general method for optimization with semi-infinite constraints that involve a linear combination of functions, focusing on the case of the exponential function. Each function is lower and upper bounded on sub-intervals by…

Optimization and Control · Mathematics 2014-01-13 Bogdan Dumitrescu , Bogdan C. Sicleru , Florin Avram

Spectral polynomial approximation of smooth functions allows real-time manipulation of and computation with them, as in the Chebfun system. Extension of the technique to two-dimensional and three-dimensional functions on hyperrectangles has…

Numerical Analysis · Mathematics 2019-01-21 Kevin W. Aiton , Tobin A. Driscoll

In this paper we present an algorithm to fit data via exponentials when the error is measured using the max-norm. We prove the necesssary results to show that the algorithm will converge to the best approximation no matter the dataset.

Nowadays, low-rank approximations of matrices are an important component of many methods in science and engineering. Traditionally, low-rank approximations are considered in unitary invariant norms, however, recently element-wise…

Numerical Analysis · Mathematics 2026-05-15 Stanislav Morozov , Dmitry Zheltkov , Alexander Osinsky

The paper deals with a special filtered approximation method, which originates interpolation polynomials at Chebyshev zeros by using de la Vall\'ee Poussin filters. These polynomials can be an useful device for many theoretical and…

Numerical Analysis · Mathematics 2020-08-04 Donatella Occorsio , Woula Themistoclakis

Given pointwise samples of an unknown function belonging to a certain model set, one seeks in Optimal Recovery to recover this function in a way that minimizes the worst-case error of the recovery procedure. While it is often known that…

Numerical Analysis · Mathematics 2023-08-01 Simon Foucart

The motivation of this paper is the development of an optimisation method for solving optimisation problems appearing in Chebyshev rational and generalised rational approximation problems, where the approximations are constructed as ratios…

Optimization and Control · Mathematics 2020-11-06 R. Díaz Millán , Nadezda Sukhorukova , Julien Ugon

We present a new algorithm for finding isolated zeros of a system of real-valued functions in a bounded interval in $\mathbb{R}^n$. It uses the Chebyshev proxy method combined with a mixture of subdivision, reduction methods, and…

The problem of solving linear systems is one of the most fundamental problems in computer science, where given a satisfiable linear system $(A,b)$, for $A \in \mathbb{R}^{n \times n}$ and $b \in \mathbb{R}^n$, we wish to find a vector $x…

Data Structures and Algorithms · Computer Science 2021-06-25 Mitali Bafna , Nikhil Vyas

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

Unions of graph multiplier operators are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators. The proposed method features…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-08-01 David I Shuman , Pierre Vandergheynst , Daniel Kressner , Pascal Frossard

We consider multicriteria problems of evaluating absolute ratings (scores, priorities, weights) of given alternatives for making decisions, which are compared in pairs under several criteria. Given matrices of pairwise comparisons of…

Optimization and Control · Mathematics 2026-01-27 Nikolai Krivulin

Chebyshev interpolation polynomials exhibit the exponential approximation property to analytic functions on a cube. Based on the Chebyshev interpolation polynomial approximation, we propose iterative polynomial approximation algorithms to…

Signal Processing · Electrical Eng. & Systems 2025-04-22 Cheng Cheng , Qiyu Sun , Cong Zheng

This article is concerned with an extension of univariate Chebyshev polynomials of the first kind to the multivariate setting, where one chases best approximants to specific monomials by polynomials of lower degree relative to the uniform…

Optimization and Control · Mathematics 2024-10-29 Mareike Dressler , Simon Foucart , Mioara Joldes , Etienne de Klerk , Jean Bernard Lasserre , Yuan Xu

It is well known that every Chebyshev linear approximation problem can be reduced to a linear program. In this paper we show that conversely every linear program can be reduced to a Chebyshev linear approximation problem.

Optimization and Control · Mathematics 2007-05-23 Leonid N. Vaserstein