Diagonalization of Matrices: Confusion about Eigenvalues and Eigenvectors

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Homework Help Overview

The discussion revolves around the diagonalization of a real symmetric matrix, specifically focusing on the construction of a matrix R with given eigenvalues and corresponding normalized eigenvectors. Participants express confusion regarding the correct formulation for R in terms of the matrices of eigenvalues and eigenvectors, with references to different conventions found in various texts.

Discussion Character

  • Conceptual clarification, Assumption checking, Mixed

Approaches and Questions Raised

  • Participants explore the relationship between the matrices of eigenvalues and eigenvectors, questioning whether R is defined as VEV^T or V^TEV. There are attempts to clarify the conditions under which these formulations hold true, particularly in the context of orthonormal eigenvectors.

Discussion Status

The discussion is ongoing, with participants providing insights and corrections regarding the diagonalization process. Some guidance has been offered about the implications of orthonormality of eigenvectors on the formulation of R, but no consensus has been reached on the best approach or the validity of certain statements.

Contextual Notes

Participants note discrepancies in various textbooks regarding the diagonalization process, leading to confusion about the correct mathematical conventions. The discussion also highlights the importance of the arrangement of eigenvectors in the matrix used for diagonalization.

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Homework Statement



Ok so

I have to construct a real symmetric matrix R whose eigenvalues are 2,1,-2 and who corresponding normalized eigenvectors are bla bla bla..

So let the matrix of eigenvalues down diagonal be E and matrix of eigen vectors be V

Is R = VEV^T or R = V^TEV??

How am i meant to know..? Different books are saying different things!

In my notes it says that for unitary U, A = Udagger A' U where A' is the diagonalised matrix, but elsewhere it says the opposite! ahhhh


Homework Equations





The Attempt at a Solution

 
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Since [tex]VEV^T[/tex] is real and symmetric, it's actually equal to [tex]V^TEV[/tex]. [STRIKE]The point is that if a matrix S diagonalizes a matrix M, then so does S-1.[/STRIKE]

Edit: Last sentence is untrue.
 
Last edited:
fzero said:
The point is that if a matrix S diagonalizes a matrix M, then so does S-1.

Thanks. Is this true for any matrix S and M?

Can you recommend and good books on this/websites?

Thanks again!
 
bon said:
Thanks. Is this true for any matrix S and M?

Sorry, I was actually wrong about that. You need to assemble the eigenvectors into the matrix S in a particular way, and that will determine whether S-1 M S or S M S-1 is diagonal in the most general case.

Can you recommend and good books on this/websites?

Thanks again!

I'm not so familiar with current texts, but I found this thread:

https://www.physicsforums.com/showthread.php?t=429607&highlight=linear+algebra+text

which has some online texts.
 
If E is a diagonal matrix with eigenvalues on the main diagonal and V is the matrix with corresponding eigenvectors as columns, then [itex]R= VEV^{-1}[/itex] is a matrix having those eigenvalues and eigenvectors. If you choose the eigenvectors to be "orthonormal" (each has length 1 and is perpendicular to the others) then [itex]V^{-1}= V^T[/itex] so [itex]R= VEV^T[/itex].
 
fzero said:
Sorry, I was actually wrong about that. You need to assemble the eigenvectors into the matrix S in a particular way, and that will determine whether S-1 M S or S M S-1 is diagonal in the most general case.
.

I'm trying to understand this a bit better..

so let M' be the diagonalised matrix of eigenvectors.. obviously AT = A if it is diagonal

if the eigenvectors that make up M are orthonormal, then surely either S-1MS or SMS-1 will work..

If the eigenvectors are not orthonormal, which one will be the one that gives the diagonalized version of M and why?

Thanks
 
HallsofIvy said:
If E is a diagonal matrix with eigenvalues on the main diagonal and V is the matrix with corresponding eigenvectors as columns, then [itex]R= VEV^{-1}[/itex] is a matrix having those eigenvalues and eigenvectors. If you choose the eigenvectors to be "orthonormal" (each has length 1 and is perpendicular to the others) then [itex]V^{-1}= V^T[/itex] so [itex]R= VEV^T[/itex].

Thanks. Sorry, I thought I'd just quote you too in case you are able to help with my above question..

Thanks.
 
bon said:
I'm trying to understand this a bit better..

so let M' be the diagonalised matrix of eigenvectors.. obviously AT = A if it is diagonal

if the eigenvectors that make up M are orthonormal, then surely either S-1MS or SMS-1 will work..

If the eigenvectors are not orthonormal, which one will be the one that gives the diagonalized version of M and why?

Thanks

I'll try to explain, but I'm not sure that I remember a really clean way to get this. Let [tex]\{ \mathbf{v}_i \}[/tex] be an orthonormal basis for M with associated eigenvalues [tex]\lambda_i[/tex]. Let S be the matrix that has the [tex]\mathbf{v}_i[/tex] as its columns, so

[tex]S = \begin{pmatrix} \mathbf{v}_1 & \mathbf{v}_2 & \cdots & \mathbf{v}_n \end{pmatrix}.[/tex]

By construction[tex]S^{-1} = S^T = \begin{pmatrix} \mathbf{v}_1^T \\ \mathbf{v}_2^T \\ \vdots \\ \mathbf{v}_n^T \end{pmatrix},[/tex]

which is the matrix with the [tex]\mathbf{v}_i^T[/tex] as its rows.

Then

[tex]M S = \begin{pmatrix}\lambda_1 \mathbf{v}_1 & \lambda_2\mathbf{v}_2 & \cdots &\lambda_n \mathbf{v}_n \end{pmatrix}[/tex]

and

[tex]S^T M S = \text{diag}~(\lambda_1, \ldots, \lambda_n) \equiv \Lambda.[/tex]

From this we conclude that, with the given choice of S,

[tex]M = S \Lambda S^T,[/tex]

while

[tex]S^T \Lambda S = M^T.[/tex]

In the case of your original question, R was symmetric, so these were equal. Also note that [tex]S M S^T[/tex] has no obvious special form.
 
Last edited:
thanks a lot!
 

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