Spectral Shape Preserving Approximation

TL;DR

This paper presents a novel algorithm for joint approximation of functions and derivatives that preserves shape, introduces structured orthogonal polynomials, and explores their applications in wavelet generation and extrapolation.

Contribution

It introduces a new shape-preserving approximation algorithm using alternative orthogonal polynomials and develops structured orthogonal polynomials with applications in wavelets.

Findings

Algorithm exhibits shape-preserving properties.

Structured orthogonal polynomials can generate wavelet functions.

Method enables shape-preserving extrapolation.

Abstract

We introduce an algorithm of joint approximation of a function and its first derivative by alternative orthogonal polynomials on the interval [0,1].The algorithm exhibits properties of shape preserving approximation for the function. A weak formulation of approximation is presented. An example on shape preserving extrapolation is given. The weak form is reduced for approximation on a discrete set of abscissas. Also, we introduce a new system of orthogonal functions with nice properties - structured orthogonal polynomials - and show that the system can be employed for a different kind of joint approximation of a function and its first derivative and may have property of shape preserving approximation. In addition, we show that structured orthogonal polynomials generate wavelet functions We complement these results with definition of structured semi-orthogonal polynomials and introduce wavelet basis functions.