Diagonalization of Matrices: Confusion about Eigenvalues and Eigenvectors

In summary, the conversation discusses constructing a real symmetric matrix with specific eigenvalues and corresponding eigenvectors. The discussion also touches upon the use of unitary matrices and the assembly of eigenvectors to determine whether S-1 M S or S M S-1 is diagonal. The conversation ends with a clarification on the relationship between M and S in regards to the previous discussion.
  • #1
bon
559
0

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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  • #2
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.
 
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  • #3
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!
 
  • #4
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.
 
  • #5
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].
 
  • #6
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
 
  • #7
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.
 
  • #8
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.
 
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  • #9
thanks a lot!
 

1. What is diagonalization of matrices?

Diagonalization of matrices is a process in linear algebra where a square matrix is transformed into a diagonal matrix by finding a basis of eigenvectors for the matrix.

2. Why is diagonalization of matrices important?

Diagonalization of matrices is important because it simplifies the manipulation and analysis of matrices. It also helps in solving systems of linear equations, computing powers of matrices, and finding inverse matrices.

3. How is diagonalization of matrices done?

Diagonalization of matrices is done by finding the eigenvalues and eigenvectors of a matrix, constructing a diagonal matrix using the eigenvalues, and then finding the transformation matrix that maps the original matrix to the diagonal matrix.

4. What are the applications of diagonalization of matrices?

Diagonalization of matrices has various applications in fields such as physics, engineering, and economics. It is used in solving systems of differential equations, analyzing circuits, and optimizing algorithms, among others.

5. Can all matrices be diagonalized?

No, not all matrices can be diagonalized. A matrix can only be diagonalized if it has a complete set of linearly independent eigenvectors. Matrices with repeated eigenvalues or non-real eigenvalues cannot be diagonalized.

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