From Gaussian Quadrature to Chebyshev Quadrature

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Discussion Overview

The discussion centers around the relationship between Gaussian Quadrature and Chebyshev Quadrature, exploring definitions, terminology, and the process of simplification from one to the other. Participants express confusion regarding the distinctions and similarities between these two quadrature methods.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant expresses confusion about whether Gauss-Chebyshev Quadrature is the same as Chebyshev Quadrature and seeks clarification on simplifying Gaussian Quadrature to Chebyshev Quadrature.
  • Another participant provides a definition of Gaussian Quadrature, emphasizing its exactness for polynomials of degree 2n − 1 or less and discusses the role of weighting functions in the integration process.
  • A different participant cites a PDF of lecture notes that defines Chebyshev quadrature as being based on Chebyshev polynomials, suggesting a specific mathematical foundation.
  • One participant mentions finding a proof of Chebyshev quadrature in a book, indicating the difficulty in locating reliable information on the topic due to the prevalence of similar subjects in search results.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the definitions or equivalences of Gaussian and Chebyshev Quadrature, and multiple competing views remain regarding their relationship and simplification processes.

Contextual Notes

Participants reference various sources, including Wikipedia and lecture notes, but there are unresolved questions about the definitions and applications of the quadrature methods discussed. The discussion reflects a lack of clarity in the terminology and the mathematical foundations involved.

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Hi,

I'm studying about Chebyshev Quadrature and i found so little and confused information about this.
I don't know if Gauss-Chebyshev Quadrature is the same of Chebyshev Quadrature.
The only good information that i found was from Wolfram:

http://mathworld.wolfram.com/ChebyshevQuadrature.html

And there is write Chebyshev Quadrature is a simplification of Gaussian quadrature. So here is my question: How can i simplify from Gaussian Quadrature to Chebyshev Quadrature?
 
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You ask an interesting question about terminology. I don't know the answer, but I think it would help to state the question explicitly rather than expecting readers to follow links.

The Wikipedia article on Gaussian Quadrature states:

An n-point Gaussian quadrature rule, named after Carl Friedrich Gauss, is a quadrature rule constructed to yield an exact result for polynomials of degree 2n − 1 or less by a suitable choice of the points x_i and weights w_i for i = 1,...,n. The domain of integration for such a rule is conventionally taken as [−1, 1], so the rule is stated as

\int_{-1}^{1} f(x) dx \approx \sum_{i=1}^n w_i f(x_i)

...if the integrated function can be written as f(x) = W(x) g(x), where g(x) is approximately polynomial, and W(x) is known, then there are alternative weights {w'}_i such that

\int_{-1}^1 f(x)\,dx = \int_{-1}^1 W(x) g(x)\,dx \approx \sum_{i=1}^n w_i' g(x_i)

Common weighting functions include W(x)=(1-x^2)^{-1/2} (Chebyshev–Gauss)...


The question is whether that definition is equivalent to the one on the Wolfram site which defines Chebyshev Quadrature as:

A Gaussian quadrature-like formula for numerical estimation of integrals. It uses weighting function W(x)=1 in the interval [-1,1] and forces all the weights to be equal. The general formula is
\int_{-1}^1 f(x)dx=\frac{2}{n} \sum_{i=1}^n f(x_i)<br />

where the abscissas x_i are found by taking terms up to y^n in the Maclaurin series of
s_n(y)=exp(1/2n[-2+ln(1-y)(1-\frac{1}{y})+ln(1+y)(1+\frac{1}{y})])

and then defining
G_n(x)=x^n s_n(\frac{1}{x})

The roots of G_n(x) then give the abscissas.

I had to do the LaTex manually instead of a straight cut-and-past. I hope I haven't introduced any typos.
 
Thanks Stephen Tashi.

Finally i found the proof. Chebyshev quadrature is really hard to find because always when you google it other similar topics appears. So the book i found this information is: Introduction to Numerical Analysis - F. B. Hildebrand
 

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