Improving the convergence analysis of linear subdivision schemes

Fuente: arXiv
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Autori principali: Dyn, Nira, Sharon, Nir
Natura: Preprint
Pubblicazione: 2024
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author Dyn, Nira
Sharon, Nir
author_facet Dyn, Nira
Sharon, Nir
contents This work presents several new results concerning the analysis of the convergence of binary, univariate, and linear subdivision schemes, all related to the {\it contractivity factor} of a convergent scheme. First, we prove that a convergent scheme cannot have a contractivity factor lower than half. Since the lower this factor is, the faster is the convergence of the scheme, schemes with contractivity factor $\frac{1}{2}$, such as those generating spline functions, have optimal convergence rate. Additionally, we provide further insights and conditions for the convergence of linear schemes and demonstrate their applicability in an improved algorithm for determining the convergence of such subdivision schemes.
format Preprint
id arxiv_https___arxiv_org_abs_2405_09414
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Improving the convergence analysis of linear subdivision schemes
Dyn, Nira
Sharon, Nir
Numerical Analysis
This work presents several new results concerning the analysis of the convergence of binary, univariate, and linear subdivision schemes, all related to the {\it contractivity factor} of a convergent scheme. First, we prove that a convergent scheme cannot have a contractivity factor lower than half. Since the lower this factor is, the faster is the convergence of the scheme, schemes with contractivity factor $\frac{1}{2}$, such as those generating spline functions, have optimal convergence rate. Additionally, we provide further insights and conditions for the convergence of linear schemes and demonstrate their applicability in an improved algorithm for determining the convergence of such subdivision schemes.
title Improving the convergence analysis of linear subdivision schemes
topic Numerical Analysis
url https://arxiv.org/abs/2405.09414