Question about the roots of Chebyshev polynomials

In summary, the person is seeking help with constructing an optimization problem using the Chebyshev pseudospectral method. The article suggests using the expression tk=cos(πk/N) to calculate the zeros of the Chebyshev polynomial of any order. However, when using N=2, the resulting nodes are not the roots of the polynomial of order 3 as expected. The person is using the cosine expression to find collocation points in their model, but is not sure if this is the correct approach. The article does not claim that the cosine expression yields the roots of the polynomial, but rather interpolation points. The person is seeking clarification on the role of the cosine expression in relation to the roots of the polynomial.
  • #1
confused_engineer
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TL;DR Summary
I need to calculate the roots of the Chebyshev polynomials, but the results do not correspond with the theory
Hello everyone. I am trying to construct an optimization problem using Chebyshev pseudospectral method as described in this article. For that, I need to calculate the zeros of the Chebyshev polynomial of any order. In the article is sugested to do it as

tk=cos(πk/N) k=0, ..., N

However, if I assign the value 2 to N, the nodes are -1, 0 and 1.

The Chebyshev polynomial of order 3 is 4t3-3t and it's roots are -√3/2, 0 and √3/2, this doesn't correspond with the results of the expression above.

In my model I am using the first expression, the cosine, to find the collocation points instead of using the roots of the polynomial of order N. The solver returns an error or an incorrect optimal solution based on the number of colocation points. Can someone please tell me if I am doing it right by choosing my collocation points as the cosine or if I should try to find the roots of the polynomials instead?

Any answer is appreciated.
Thanks for reading.
Regards.
 
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  • #2
The article does not claim these are the roots of the Chebyshev polynomials, but interpolation points (as far as I understand, the article claims that these are the points where these polynomials attain extrema).
 
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  • #3
First of all thanks for your answer.

If I understand propperly, the node points are the roots of the polynomial of order N? but then, what is the use of the cosine?

I have plotted the values if the cosine for N=60 and these values cluster arround the endpoints of the interval [-1, 1], I think that is the reason why I have confused the cosine with the nodes.

Regards.
 

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1. What are Chebyshev polynomials?

Chebyshev polynomials are a type of orthogonal polynomial that are commonly used in mathematics and physics. They are named after the Russian mathematician Pafnuty Chebyshev and have a wide range of applications, including in approximation theory and numerical analysis.

2. What is the significance of the roots of Chebyshev polynomials?

The roots of Chebyshev polynomials are important because they are evenly spaced on the interval [-1,1], making them useful for polynomial interpolation and approximation. They also have a unique property where the maximum absolute value of the polynomial occurs at the roots, which can be used for minimizing errors in numerical calculations.

3. How are the roots of Chebyshev polynomials calculated?

The roots of Chebyshev polynomials can be calculated using various methods, such as the Chebyshev-Gauss quadrature method or the Newton-Raphson method. These methods involve iterative processes that converge to the roots of the polynomial.

4. What is the relationship between Chebyshev polynomials and trigonometric functions?

Chebyshev polynomials have a close relationship with trigonometric functions, particularly cosine and sine. In fact, the first and second kind of Chebyshev polynomials can be expressed in terms of cosine and sine functions, respectively. This relationship is useful in various applications, such as in Fourier series and spectral methods.

5. Are there any real-world applications of Chebyshev polynomials?

Yes, there are many real-world applications of Chebyshev polynomials. Some examples include in signal processing, image compression, and solving differential equations. They are also used in physics, such as in the study of planetary motion and quantum mechanics.

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