Orthogonal eigenvectors and Green functions

In summary, the conversation discusses diagonalizing a hermitian operator with both discrete and continuous spectrum. The question is whether eigenvectors with eigenvalues in each spectrum are mutually orthogonal. The answer is yes, but this was initially questioned due to a mistake in numerical calculation. The conversation also mentions using a perturbative expansion and the Green operator to calculate eigenvalues and eigenvectors, ultimately leading to the discovery that the inner product between ψ and χ is zero.
  • #1
paolorossi
24
0
Hi you all. I have to diagonalize a hermitian operator (hamiltonian), that has both discrete and continuous spectrum. If ψ is an eigenvector with eigenvalue in the continuous spectrum, and χ is an eigenvector with eigenvalue in the discrete spectrum, is correct to say that ψ and χ are always mutually orthogonal? I think the answer is yes. But if I numerically calculate the inner product between ψ and χ, then I find that this is far from zero.

PS
I work in this way.
I calculate the eigenvalues and eigenvectors from the Green operator.
Specifically, I have the hamiltonian operator

H = H0 + V

H0 has only continuous spectrum. Using a perturbative expansion, I find the Green operator

G(z) = 1/(z-H)

in terms of V and of the Green operator of H0

G0(z) = 1/(z-H0)

So I find that G(z) has a branch cut and one simple pole. This is consistent with various works and books. Then I calculate the eigenvalues and the corrispondent eigenvectors. So I express ψ and χ in terms of a common basis, and using the Fourier coefficients I can calculate the inner product.
 
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  • #2
ok I solve it. I have calculated analytically the inner product and I see that it is zero. In fact, I had made ​​a mistake in the numerical calculation.
 

1. What are orthogonal eigenvectors?

Orthogonal eigenvectors are a set of vectors that are perpendicular to each other and have the property that when multiplied by a matrix, the resulting vector is a scalar multiple of the original vector. They are important in linear algebra and are used in various applications, such as in solving systems of linear equations and understanding the behavior of linear transformations.

2. How are orthogonal eigenvectors related to Green functions?

In the context of linear algebra, orthogonal eigenvectors are closely related to the concept of eigenfunctions, which are used in the construction of Green functions. Green functions are mathematical tools used to solve differential equations, and they can be constructed using orthogonal eigenvectors as a basis. In this way, orthogonal eigenvectors play a crucial role in the study of Green functions.

3. What are some applications of orthogonal eigenvectors and Green functions?

Orthogonal eigenvectors and Green functions have various applications in physics, engineering, and mathematics. Some examples include solving heat and wave equations, analyzing the behavior of vibrating systems, and understanding the properties of quantum systems. They are also used in data analysis and signal processing, among other fields.

4. How can one calculate orthogonal eigenvectors and Green functions?

The calculation of orthogonal eigenvectors and Green functions can be done using various methods, depending on the specific problem at hand. In general, finding orthogonal eigenvectors involves solving systems of linear equations or finding eigenvalues and corresponding eigenvectors of a matrix. Green functions can be constructed using a variety of mathematical techniques, such as Fourier transforms and Laplace transforms.

5. Are orthogonal eigenvectors and Green functions related to each other in any way?

Yes, orthogonal eigenvectors and Green functions are closely related. In many cases, orthogonal eigenvectors are used as a basis for constructing Green functions. Additionally, the eigenvalues of a matrix are often used as the arguments of Green functions. Moreover, both concepts are fundamental in linear algebra and are used in various applications, as mentioned before.

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