How do we determine the eigenvalues of B^2 if B is Hermitian?

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Homework Help Overview

The discussion revolves around the properties of eigenvalues related to a Hermitian matrix B and its square, A = B^2. The original question seeks to establish that A is positive semidefinite by demonstrating that its eigenvalues are non-negative.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants explore the relationship between the eigenvalues of B and B^2, questioning why the eigenvalues of B^2 are the squares of those of B. There is an inquiry into the implications of acting B^2 on the eigenvectors of B and whether this leads to a generalizable result.

Discussion Status

The discussion is active, with participants providing insights into the relationship between eigenvalues and eigenvectors. Some have offered alternative proofs to support the claim that the eigenvalues of A are non-negative. However, there is no explicit consensus on the generality of the results discussed.

Contextual Notes

Participants are working within the constraints of a homework problem, focusing on the properties of Hermitian matrices and their implications for eigenvalues. There is an ongoing examination of definitions and assumptions related to eigenvectors and eigenvalues.

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The question was:

If B is Hermitian show that [tex]A=B^2[/tex] is positive semidefinite.

The answer was:

[tex]B^2[/tex] has eigenvalues [tex]\lambda_1 ^2, ... \lambda_n^2[/tex]
(the square of B's eigenvalues) all non negative.

My question is:

Why do we know that [tex]B^2[/tex] has eigenvalues [tex]\lambda^2[/tex]?
 
Last edited:
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What happens if you act [tex]B^2[/tex] on the eigenvectors of [tex]B[/tex]?
 
I am not quite sure what you mean by act, but I will assume you mean multiply together. If we take:

[tex]A= \left[ \begin{array} {cc} 1 & 1 \\ 1 & 1 \end {array} \right][/tex]

Then the eigenvalues are 2 and 0.

Then eigenvectors are:

[tex]B = \left[ \begin{array} {cc} 1 & 1 \\ 1 & -1 \end {array} \right][/tex]Then [tex]AB = \left[ \begin{array} {cc} 2 & 2 \\ 0 & 0 \end {array} \right][/tex]

Wow! The values in the matrix are the eigenvalues. Is this result general? Can we prove it?
How does that help solve the original question?
 
Last edited:
Let V be any eigenvector of B, and l be any eigenvalue.

B*V= l*V by definition. Starting from that equation you should be able to determine the eigenvalues of B^2. This is what Parlyne meant.

edit: l is the corresponding eigenvalue of V.
 
heres an alternative proof to show that the eigenvalues of A are all nonnegative (and thus A is positive semidefinite). let [itex]x[/itex] be an eigenvector of A, and let [itex]\lambda[/itex] be its corresponding eigenvalue. Then we have the following.

[tex]\lambda \langle x,x \rangle = \langle \lambda x, x \rangle = \langle Ax, x \rangle = \langle B^2 x, x \rangle = \langle B^{*}Bx, x \rangle = \langle Bx, Bx \rangle \geq 0[/tex]
 
Oh gee! of course. it is obvious or should have been. Thank you.
 

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