Hyperbolic Spline Interpolation: Difference between revisions

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[[File:Hyperbolic Spline Interpolation - Space.png|1000px]]
[[File:Hyperbolic Spline Interpolation - Space.png|1000px]]


== Pareto Frontier Improvements Graph ==  
== Time-Space Tradeoff ==  


[[File:Hyperbolic Spline Interpolation - Pareto Frontier.png|1000px]]
[[File:Hyperbolic Spline Interpolation - Pareto Frontier.png|1000px]]

Revision as of 14:47, 15 February 2023

Description

The problem of restoring complex curves and surfaces from discrete data so that their shape is preserved is called isogeometric interpolation. A very popular tool for solving this problem are hyperbolic splines in tension, which were introduced in 1966 by Schweikert. These splines have smoothness sufficient for many applications; combined with algorithms for the automatic selection of the tension parameters, they adapt well to the given data. Unfortunately, the evaluation of hyperbolic splines is a very difficult problem because of roundoff errors (for small values of the tension parameters) and overflows (for large values of these parameters).�

Parameters

No parameters found.

Table of Algorithms

Name Year Time Space Approximation Factor Model Reference
Kvasov 2006 2008 $O(n^{3} log^{2}K)$ Exact Deterministic Time
V. A. Lyul’ka and A. V. Romanenko 1994 $O(n^{5})$ Exact Deterministic Time
V. A. Lyul’ka and I. E. Mikhailov 2003 $O(n^{4})$ Exact Deterministic Time
V. I. Paasonen 1968 $O(n^{5} logK)$ Exact Deterministic
P. Costantini; B. I. Kvasov; and C. Manni 1999 $O(n^{5} logK)$ $O(n)$? Exact Deterministic Time
B. I. Kvasov 2000 $O(n^{4})$ Exact Deterministic

Time Complexity Graph

Hyperbolic Spline Interpolation - Time.png

Space Complexity Graph

Hyperbolic Spline Interpolation - Space.png

Time-Space Tradeoff

Hyperbolic Spline Interpolation - Pareto Frontier.png