What are the Eigenvalues and Eigenfunctions for the Operator \hat{Q}?

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In summary, the eigenvalues of the hermitian operator are complex exponentials and the eigenvalues are not real. The spectrum is not degenerate.
  • #1
broegger
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I having trouble finding the eigenvalues and eigenfunctions for the operator

[tex]\hat{Q} = \frac{d^2}{d\phi^2}, [/tex]​

where [tex]\phi[/tex] is the azimuthal angle. The eigenfunctions are periodical,

[tex]f(\phi) = f(\phi + 2\pi), [/tex]​

which I think should put some restrictions on the eigenvalues.

I think that the eigenfunctions are complex exponentials, and that the eigenvalues are 0,-1,-2,..., but I am not sure if this is correct. Also I have to determine if the spectrum is degenerate, that is, if two (or more) distinct eigenfunctions correspond to the same eigenvalue.
 
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  • #2
You're right about the complex exponentials and wrong about the eigenvalues, but finding the e-values isn't hard, just substitute a complex exponential into the equation and see what happens. :smile:

Those numbers ...-1, 0, 1, 2, ... do parametrize the equation - they turn out to be quantum numbers, not eigenvalues. Make sure you understand why they have to be integers.

Degeneracy should follow from what the eigenvalues look like, so calculate them first.

Kane
 
  • #3
[tex] \frac{d^2 f}{d\phi^2} = Af [/tex]

Where A is an eigenvalue. Now solve this diff equation and make a clear distinction between A positive and negative. Also keep in mind that the eigenfunctions need to be periodical.

marlon
 
  • #4
Okay.Better solve the ODE and then impose the periodicity condition.

Daniel.
 
  • #5
This is my solution-attempt:

For negative eigenvalues we have:

[tex]f(\phi) = e^{-iq\phi} [/tex]​

This correspond to eigenvalues [tex]\lambda = -q^2[/tex]. Since [tex]f(\phi)[/tex] is [tex]2\pi[/tex]-periodical we have:

[tex] 1 = e^{-iq2\pi}. [/tex]​

This implies that [tex]q = 0, \pm1, \pm2, \ldots[/tex], which again implies that the eigenvalues are [tex]\lambda = 0, -1, -4, -9, \ldots[/tex]. There are no positive eigenvalues.
 
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  • #6
Are u sure...?
[tex] \frac{d^{2}f(\varphi)}{d\varphi^{2}}=\lambda f(\varphi) [/tex]

I'm getting some square roots...

Daniel.
 
  • #7
dextercioby said:
Are u sure...?

No, I'm not sure, not at all :biggrin:

From where do you get the square roots? If you differentiate twice you get [tex](-iq)^2 = -q^2[/tex]?
 
  • #8
??Well,the characteristic equation is
[tex] r^{2}=\lambda [/tex]

,with the solutions:
[tex] r_{1,2}=\pm \sqrt\lambda [/tex]

Daniel.
 
  • #9
I don't get it :( What do you get for the eigenvalues exactly?
 
  • #10
The eigenvalues need not be real a a priori, but the periodicity implies it.

Since at first the eigenvalue lambda can be anything at all, best let [itex]\lambda = -m^2[/itex], where [itex]m[/itex] can be any complex number.
 
  • #11
[tex]\hat{Q}[/tex] is hermitian, so the eigenvalues must be real - isn't that correct? If we apply the condition [tex]\lambda = -m^2[/tex], we get the eigenvalues 0, 1, 4, 9,... since the periodicity implies that [tex]m = 0, \pm1, \pm2,...[/tex] Right?
 
  • #12
broegger said:
[tex]\hat{Q}[/tex] is hermitian, so the eigenvalues must be real - isn't that correct? If we apply the condition [tex]\lambda = -m^2[/tex], we get the eigenvalues 0, 1, 4, 9,... since the periodicity implies that [tex]m = 0, \pm1, \pm2,...[/tex] Right?
Yeah, I overlooked the Hermiticity.

The eigenvalues are nonnegative as you said, so you get 0,-1,-4 etc.
 
  • #13
Thank you, Galileo. And the rest of you too :)
 
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Related to What are the Eigenvalues and Eigenfunctions for the Operator \hat{Q}?

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are used to describe the behavior of linear transformations. Eigenvalues are scalar values that represent how much a particular eigenvector is stretched or shrunk by the transformation. Eigenvectors are the corresponding vectors that are only scaled by the transformation, not rotated.

2. How are eigenvalues and eigenvectors used in data analysis?

Eigenvalues and eigenvectors are commonly used in data analysis to reduce the dimensions of a dataset. This is done by finding the eigenvectors of the dataset's covariance matrix, and then using them as new variables to represent the data. This helps to simplify the data and identify important patterns and relationships.

3. Can eigenvalues be negative?

Yes, eigenvalues can be negative. The sign of an eigenvalue is determined by the direction of the eigenvector it corresponds to. If the eigenvector points in the opposite direction of the original vector, the eigenvalue will be negative.

4. What is the relationship between eigenvalues and determinants?

The determinant of a matrix is equal to the product of its eigenvalues. This means that by finding the eigenvalues of a matrix, we can also determine its determinant. Additionally, a matrix is invertible if and only if all of its eigenvalues are non-zero.

5. How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated using different methods, such as the power method, the QR algorithm, or the Jacobi method. These methods involve finding the roots of the characteristic polynomial of the matrix, which is a polynomial that depends on the eigenvalues. Once the eigenvalues are known, the corresponding eigenvectors can be found by solving a system of linear equations.

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