Finding a matrix W such that W^t*AW = D (D is diagonal matrix)

In summary, the conversation discusses finding eigenvalues and corresponding eigenvectors for a lower triangular matrix. The eigenvalues are determined to be 1 and 0, and the corresponding eigenvectors are found to be (0 1 0) and (-1 0 1). The final part of the question involves finding a matrix W such that W^t*AW = D, where D is a diagonal matrix. The attempt at a solution involves finding a matrix P using the bases of the eigenspaces and then choosing D to be the eigenvalues in the diagonal positions corresponding with its bases in P. However, this approach does not result in the desired diagonal matrix.
  • #1
zjohnson19
4
0

Homework Statement


A = 000
010
101

Find Eigenvalues, its corresponding eigenvectors, and find a matrix W such that W^t*AW = D, where D is a diagnol matrix.(note that W^t represents the transpose of W)

Homework Equations


Eigenvalues, Eigenvectors, diagnolization[/B]

The Attempt at a Solution


Question 1 is to find eigenvalues. Since it is already a lower triangular matrix this was easy and I believe the eigenvalues are 1 and 0. The characteristic equation I got was (0-y)(1-y)(1-y).

Question 2 was to find the corresponding eigenvectors. if y1 = 1, then (A-1I) = -100
000
100

From that you get x1 = 0. x2 and x3 are free. So a basis for the eigenspace is (v1, v2) where
v1 = 0
1
0

v2 = 0
0
1

For y = 0, A -0 is just A. you get x2 = 0 and x1 = -x3 with x3 free. Therefore the corresponding eigenvector is
v = -1
0
1

Correct me if I'm wrong but I believe up to here I have done everything correct.

The final part of the question confuses me quite a bit . "find a matrix W such that W^t*AW = D, where D is a diagnol matrix."

I thought diagnolization is only possible if a matrix is invertible. Since A has 0 as an eigenvalue, it is not invertible. Did I get the eigenvectors wrong or is D not the diagnolization of A?

Edit: I cannot find a way to get the space formatting correct. Does anyone know how?
 
Last edited:
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  • #2
zjohnson19 said:

Homework Statement


A = 000
010
101

Find Eigenvalues, its corresponding eigenvectors, and find a matrix W such that W^t*AW = D, where D is a diagnol matrix.

Homework Equations


Eigenvalues, Eigenvectors, diagnolization[/B]

The Attempt at a Solution


Question 1 is to find eigenvalues. Since it is already a lower triangular matrix this was easy and I believe the eigenvalues are 1 and 0. The characteristic equation I got was (0-y)(1-y)(1-y).

Question 2 was to find the corresponding eigenvectors. if y1 = 1, then (A-1I) = -100
000
100

From that you get x1 = 0. x2 and x3 are free. So a basis for the eigenspace is (v1, v2) where
v1 = 0
1
0

v2 = 0
0
0

For y = 0, A -0 is just A. you get x2 = 0 and x1 = -x3 with x3 free. Therefore the corresponding eigenvector is
v = -1
0
1

Correct me if I'm wrong but I believe up to here I have done everything correct.

The final part of the question confuses me quite a bit . "find a matrix W such that W^t*AW = D, where D is a diagnol matrix."

I thought diagnolization is only possible if a matrix is invertible. Since A has 0 as an eigenvalue, it is not invertible. Did I get the eigenvectors wrong or is D not the diagnolization of A?

Edit: I cannot find a way to get the space formatting correct. Does anyone know how?

The zero vector is never considered as an eigenvector, so your v2 is incorrect. Not only that, you want the eigenvectors to form a basis (which is possible in the diagonalizable case), and the zero vector is never part of a basis.
 
  • #3
My bad, I had that copied down wrong(I edited it now to fix it). I had v2 as (0 0 1), not (0 0 0). So a basis for the eigenspace corresponding to lambda = 0 would be

[0] [ 0]
|1| , | 0|
[0] [ 1]

one eigenvector would be
[0]
|1|
[1]
 
  • #4
I've tried to diagnolize the matrix and I found an P and A that work(since AP = PD).

I got p from the bases of the eigenspaces.
p =
00-1
10 0
01 1

I chose D to be the eigenvalues in the diagnol positions corresponding with its bases in p
D=
100
010
000

AP=PD so this step should be fine.

I then was hoping this would work for the final answer and I could choose P as the answer. Unfortunately the transpose of P * AP does not equal this diagnol matrix.

Anyone know where to go from here?
 

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1. How do you find a matrix W that satisfies the equation W^t*AW = D?

The most common way to find a matrix W that satisfies the given equation is by using the spectral theorem. This theorem states that if A is a real symmetric matrix, then it can be diagonalized by an orthogonal matrix, and the resulting diagonal matrix will be the desired D. So, by finding the eigenvalues and eigenvectors of A, we can construct an orthogonal matrix W that satisfies the equation.

2. Can any matrix A be diagonalized by an orthogonal matrix W?

No, only real symmetric matrices can be diagonalized by an orthogonal matrix. This is because only real symmetric matrices have real eigenvalues and orthogonal eigenvectors, which are necessary for the spectral theorem to hold.

3. How many solutions are there for the equation W^t*AW = D?

If A is a real symmetric matrix, there will be infinitely many solutions for the given equation. This is because any orthogonal matrix W that diagonalizes A can be multiplied by a diagonal matrix D and still satisfy the equation. So, the set of all possible solutions will form a group under multiplication.

4. What is the significance of finding a matrix W that satisfies the given equation?

By finding a matrix W that satisfies the equation W^t*AW = D, we can transform the original matrix A into a diagonal matrix D. This can be useful in various applications, such as simplifying calculations, finding eigenvalues and eigenvectors, and solving systems of linear equations.

5. Are there any other methods to find a matrix W that satisfies the given equation?

Yes, there are other methods to find a matrix W that satisfies the equation W^t*AW = D, such as the Cholesky decomposition and the QR decomposition. These methods also involve transforming A into a diagonal matrix, but they use different matrices (lower-triangular and unitary, respectively) instead of an orthogonal matrix.

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