From Gaussian Quadrature to Chebyshev Quadrature

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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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