Polynomial meshes on algebraic sets

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED
Leokadia Bialas-Ciez , Agnieszka Kowalska , Alvise Sommariva
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引用次数: 0

Abstract

Polynomial meshes (called sometimes ‘norming sets’) allow us to estimate the supremum norm of polynomials on a fixed compact set by the norm on its discrete subset. We give a general construction of polynomial weakly admissible meshes on compact subsets of arbitrary algebraic hypersurfaces in N+1. They are preimages by a projection of meshes on compacts in N. The meshes constructed in this way are optimal in some cases. Our method can be useful also for certain algebraic sets of codimension greater than one. To illustrate applications of the obtained theorems, we first give a few examples and finally report some numerical results. In particular, we present numerical tests (implemented in Matlab), concerning the use of such optimal polynomial meshes for interpolation and least-squares approximation, as well as for the evaluation of the corresponding Lebesgue constants.
代数集上的多项式网格
多项式网格(有时称为“赋范集”)允许我们通过其离散子集上的范数来估计固定紧集中多项式的最高范数。给出了在任意代数超曲面的紧子集上多项式弱容许网格的一般构造。它们是由网格投影到N的紧集上的预像。以这种方式构建的网格在某些情况下是最优的。我们的方法也适用于余维数大于1的代数集。为了说明所得定理的应用,我们首先给出了几个例子,最后给出了一些数值结果。特别是,我们提出了数值测试(在Matlab中实现),涉及使用这种最优多项式网格进行插值和最小二乘逼近,以及相应的勒贝格常数的评估。
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来源期刊
CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
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