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You know what is a bloody great introduction to polynomial bases? Golub and Meurant (2010). They cram it into the first chapter and then do computational stuff in the subsequent chapters. It is excellent.
1 Fun things
Terry Tao on Conversions between standard polynomial bases.
Representation in terms of basis is useful for analysing the Natural exponential family with quadratic variance function (Morris 1982).
1.1 Well known facts
Xiu and Karniadakis (2002) mention the following “Well known facts”:
All orthogonal polynomials
It is well known that continuous orthogonal polynomials satisfy the second-order differential equation
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There’s a well-known theorem in complex analysis that says that if p is a polynomial, then the zeros of its derivative p′ lie inside the convex hull of the zeros of p.
2 Zoo
This list is extracted from a few places including Xiu and Karniadakis (2002).
Family | Orthogonal wrt | |
---|---|---|
Monomial | n/a | |
Bernstein | n/a | |
Legendre | ||
Hermite | ||
Laguerre | ||
Jacobi | ||
Gegenbauer | cf Funk-Hecke formula; special case of Jacobi | |
Chebyshev | Special case of Gegenbauer | |
Charlier | Poisson distribution | |
Meixner | negative binomial distribution | |
Krawtchouk | binomial distribution | |
Hahn | hypergeometric distribution | |
??? | Unit ball | Does this have a name? |
3 Tools
- Orcuslc/OrthNet: TensorFlow, PyTorch and Numpy layers for generating Orthogonal Polynomials
- Julia’s ApproxFun.jl
(see a write-up in my julia notebook.
- Chebfun is a classic MATLAB toolkit for working with functions represented by Chebyshev polynomials.