Question about weights using Chebyshev polynomials as quadrature

In summary, the article Differentiates between the case of odd and even number of collocation points. If N is even, then the N+1 collocation points, including 0, form a vector of odd length. The weights are calculated as ws=wN-s=... for s=1, 2, ..., N/2. Meanwhile, w0 and wN are calculated on a different way. This in turn means that one of the elements is calculated twice, as shown in the following example: N=6; N+1=7; N/2=3; s=1, 2, 3; N-s=6-1=5, 6-2=4, 6-3=3
  • #1
confused_engineer
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2
Hello everyone.

I am studying this article since I am interested in optimization. The article makes use of Clenshaw–Curtis quadrature scheme to discretize the integral part of the cost function to a finite sum using Chebyshev polynomials.

The article differentiates between the case of odd and even number of collocation points. In equation 27 and 28 (fourth page), the case of N even is discussed. If N is even, then the N+1 collocation points, including 0, form a vector of odd length.

Then weights are calculated as ws=wN-s=... for s=1, 2, ..., N/2. Meanwhile, w0 and wN are calculated on a different way.

This in turn means that one of the elements is calculated twice, as shown in the following example:

N=6; N+1=7; N/2=3; s=1, 2, 3; N-s=6-1=5, 6-2=4, 6-3=3.

As you can see, the fourth element of the vector, number 3, appears two times. I find this weird. Can someone please tell me if I am understanding the article wrong or if this is intended to happen?

Thanks for reading.
Regards.
 
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  • #2
I do not see where the problem is. For ##N=6## we have ##w_0=w_6=1/35## and then formulas for ##w_1=w_5##, ##w_2=w_4##, and ##w_3##. Since ##s=3= N-s=6-3## and the formula for ##w_3## only depends on the given number ##N## and ##s=3##, where is it defined twice?
 
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  • #3
fresh_42 said:
I do not see where the problem is. For ##N=6## we have ##w_0=w_6=1/35## and then formulas for ##w_1=w_5##, ##w_2=w_4##, and ##w_3##. Since ##s=3= N-s=6-3## and the formula for ##w_3## only depends on the given number ##N## and ##s=3##, where is it defined twice?
First of all, thanks for answering my question.

I am confused because the article tallks about ωs and ωN-s and I find extrange that following the paper one arrives to ω33=4/N*... for this particular example.

Therefore, I understand from your answer that ω33=4/N*... is the weight of the fourth element of the vector. I thought that since I had ω33, the middle element might have twice the weight.

Sorry if I haven't expressed myself clearly.
Thanks again for your answer.
 
  • #4
No, it was only a way to avoid writing an extra line for ##\omega_{N/2}##. ##N/2 = N-N/2## for even ##N## so it is only defined once.
 
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  • #5
fresh_42 said:
No, it was only a way to avoid writing an extra line for ##\omega_{N/2}##. ##N/2 = N-N/2## for even ##N## so it is only defined once.
Then is all clear. Thank you very much.
 

1. What are Chebyshev polynomials and how are they used in quadrature?

Chebyshev polynomials are a type of orthogonal polynomial that are commonly used in numerical integration, or quadrature, methods. They are defined as a sequence of polynomials that satisfy a specific recurrence relation, and they have the property of minimizing the error in approximating a function over a given interval. In quadrature, Chebyshev polynomials are used as the basis functions for approximating the integral of a function over a certain interval.

2. How do Chebyshev quadrature methods compare to other numerical integration techniques?

Chebyshev quadrature methods are known for their efficiency and accuracy in approximating integrals. They often outperform other numerical integration techniques, such as the trapezoidal rule or Simpson's rule, especially for functions that have singularities or oscillatory behavior. Additionally, Chebyshev quadrature methods are well-suited for high-dimensional integrals, making them a popular choice in scientific computing.

3. Can Chebyshev quadrature be used for any type of function?

Chebyshev quadrature methods are most effective for smooth functions that do not have any singularities or discontinuities. However, they can still be used for functions with these features by adjusting the number of quadrature points or using a modified version of the Chebyshev polynomials. In general, Chebyshev quadrature is best suited for functions that are well-behaved over the interval of integration.

4. How do you determine the optimal number of quadrature points to use with Chebyshev polynomials?

The number of quadrature points used in a Chebyshev quadrature method depends on the desired level of accuracy. Generally, the more quadrature points used, the more accurate the approximation will be. However, there is a trade-off between accuracy and computational cost, so the optimal number of points will depend on the specific problem at hand. Some techniques, such as adaptive quadrature, can automatically determine the optimal number of points for a given function.

5. Are there any limitations to using Chebyshev quadrature methods?

While Chebyshev quadrature methods are highly effective for many types of integrals, there are some limitations to consider. As mentioned before, they are best suited for smooth functions with no singularities. Additionally, they may not perform well for functions with rapidly changing behavior or for integrals over unbounded intervals. In these cases, other numerical integration techniques may be more appropriate.

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