Local Corner Cutting and the Smoothness of the Limiting Curve

Abstract

It was proved in an earlier work that corner cutting of any kind converges to a Lipschitz-continuous curve, but the question of how one might guarantee that the limiting curve be smoother than that was not considered there. Recently, Gregory and Qu GQ took up this question and established sufficient conditions for a certain systematic and local corner cutting scheme to give a limiting curve in C. Since GQ use the same parametrization of the successive broken lines that made the argument in B so simple, I became intrigued and took a look at what one might say in greater generality. Specifically, I looked for conditions under which continuous differentiability of the limiting curve could be inferred from the fact that the corners of the broken lines flatten out eventually. It is the purpose of this note to prove that the limit of any 'local' corner cutting scheme is in C provided the corners of the broken lines become increasingly flatter. A simple example is given to show that this condition is not necessary, while another simple example shows that, without 'localness', the condition is not sufficient, in general. Finally, as an application, Gregory and Qu's nice argument in GQ is redone.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1988
Accession Number
ADA204092

Entities

People

  • Carl R. de Boor

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Continents
  • Contracts
  • Convergence
  • Geographic Regions
  • Guarantees
  • Infinite Series
  • Interpolation
  • Intervals
  • Military Research
  • North America
  • North Carolina
  • Sequences
  • Standards
  • Step Functions
  • United States
  • Wisconsin

Readers

  • Calculus or Mathematical Analysis
  • Educational Psychology
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks