orthogonal polynomials

1 Orthogonal Polynomials

Polynomials of degree $n$ are analytic functions that can be written in the form

p_{n}(x)=a_{0}+a_{1}x+a_{2}x^{2}+\cdots+a_{n}x^{n}

They can be differentiated and integrated for any value of $x$ , and are fully determined by the $n+1$ coefficients $a_{0}\ldots a_{n}$ . For this simplicity they are frequently used to approximate more complicated or unknown functions. In approximations, the necessary degree $n$ of the polynomial is not normally defined by criteria other than the quality of the approximation.

Using polynomials as defined above tends to lead into numerical difficulties when determining the $a_{i}$ , even for small values of $n$ . It is therefore customary to stabilize results numerically by using orthogonal polynomials over an interval $[a,b]$ , defined with respect to a positive weighting function $W(x)>0$ by

\int_{a}^{b}p_{n}(x)p_{m}(x)W(x)dx=0\;\text{for}\;n\neq m

Orthogonal polynomials are obtained in the following way: define the scalar product.

(f,g)=\int_{a}^{b}f(x)g(x)W(x)dx

between the functions $f$ and $g$ , where $W(x)$ is a weight factor. Starting with the polynomials $p_{0}(x)=1$ , $p_{1}(x)=x$ , $p_{2}(x)=x^{2}$ , etc., from the Gram-Schmidt decomposition one obtains a sequence of orthogonal polynomials $q_{0}(x),q_{1}(x),\ldots$ , such that $(q_{m},q_{n})=N_{n}\delta_{mn}$ . The normalization factors $N_{n}$ are arbitrary. When all $N_{i}$ are equal to one, the polynomials are called orthonormal.

Some important orthogonal polynomials are:

$a$	$b$	$W(x)$	name
-1	1	1	Legendre polynomials
-1	1	$(1-x^{2})^{-1/2}$	Chebyshev polynomials
$-\infty$	$\infty$	$e^{-x^{2}}$	Hermite polynomials

Orthogonal polynomials of successive orders can be expressed by a recurrence relation

p_{n}=(A_{n}+B_{n}x)p_{n-1}+C_{n}p_{n-2}

This relation can be used to compute a finite series

a_{0}p_{0}+a_{1}p_{1}+\cdots+a_{n}p_{n}

with arbitrary coefficients $a_{i}$ , without computing explicitly every polynomial $p_{j}$ (Horner’s Rule).

Chebyshev polynomials $T_{n}(x)$ are also orthogonal with respect to discrete values $x_{i}$ :

\sum_{i}T_{n}(x_{i})T_{m}(x_{i})=0\;\text{for}\;n<m\leq M

where the $x_{i}$ depend on $M$ .

For more information, see [2, 3].

References

1 Originally from The Data Analysis Briefbook (http://rkb.home.cern.ch/rkb/titleA.htmlhttp://rkb.home.cern.ch/rkb/titleA.html)
2 M. Abramowitz and I.A. Stegun (Eds.), Handbook of Mathematical Functions, National Bureau of Standards, Dover, New York, 1974.
3 W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Numerical Recipes in C, Second edition, Cambridge University Press, 1995. (The same book exists for the Fortran language). There is also an Internet version which you can work from.

Title	orthogonal polynomials
Canonical name	OrthogonalPolynomials
Date of creation	2013-03-22 12:06:20
Last modified on	2013-03-22 12:06:20
Owner	akrowne (2)
Last modified by	akrowne (2)
Numerical id	11
Author	akrowne (2)
Entry type	Definition
Classification	msc 42C05
Classification	msc 33D45
Related topic	OrthogonalMatrices
Related topic	Orthonormal