Eigenvalues/vectors of Hermitian and corresponding unitary

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In summary, the conversation discusses the relationship between the eigenvectors and eigenvalues of a Hermitian matrix M and its corresponding unitary matrix K. It is shown that the two matrices have the same eigenvalues, but the eigenvectors may be different due to the change of basis caused by the unitary transformation.
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nomadreid
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Given that any Hermitian matrix M can be transformed into a unitary matrix K = UMU, for some unitary U, where U is the adjoint of U, what is the relationship (if any) between the eigenvectors and eigenvalues (if any) of the Hermitian matrix M and the eigenvectors and eigenvalues (if any) of the corresponding unitary matrix K?
 
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The unitary matrix U, and so its adjoint, has determinant 1 so [itex]det(K- \lambda)= det(M-\lambda)[/itex]. That shows that they have the same eigenvalues.
 
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Super. S, does that also show that they then have the same eigenvectors?
 
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That depends upon what you mean by "the same". We can think of U as a "change of basis" operator. In that sense, eigenvectors of K are the eigenvectors of A written in a different basis. If you are thinking of the vectors as just "list of numbers", then, no, eigenvectors of K will have different numbers because you are writing the vector in a different basis.
 
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thanks, HallsofIvy. Looking at it that way is very enlightening. That was a great help. :) Understanding is seeping in...
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used to describe the behavior of linear transformations. Eigenvalues are scalar values that represent how a linear transformation affects the magnitude of a vector, while eigenvectors are the corresponding vectors that are only scaled by the transformation.

2. What is a Hermitian matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. This means that the matrix is symmetric about its main diagonal and all of its elements are complex conjugates of each other.

3. What is a unitary matrix?

A unitary matrix is a square matrix whose inverse is equal to its conjugate transpose. This means that the matrix preserves the length of vectors and the angle between them, making it a type of orthogonal transformation in complex vector spaces.

4. How are eigenvalues and eigenvectors related to Hermitian and unitary matrices?

Hermitian and unitary matrices have special properties that make their eigenvalues and eigenvectors particularly useful. For Hermitian matrices, all eigenvalues are real and the corresponding eigenvectors are orthogonal. For unitary matrices, the eigenvalues have a magnitude of 1 and the eigenvectors are also orthogonal.

5. What applications do eigenvalues and eigenvectors of Hermitian and unitary matrices have?

Eigenvalues and eigenvectors of Hermitian and unitary matrices have various applications in physics, engineering, and computer science. They are used in quantum mechanics to describe the energy states of systems, in signal processing for filtering and noise reduction, and in data analysis for dimensionality reduction and feature extraction.

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