Linear operators, eigenvalues, diagonal matrices

In summary: Yes, that is correct. The matrix would be:\begin{bmatrix}2 & 0 & 0 \\ 0 & 2 & 0 \\ 0 & 0 & 3 \end{bmatrix}Glad to help!
  • #1
bjgawp
84
0
So I have a couple of questions in regards to linear operators and their eigenvalues and how it relates to their matrices with respect to some basis.

For example, I want to show that given a linear operator T such that [tex]T(x_1,x_2,x_3) = (3x_3, 2x_2, x_1)[/tex] then T can be represented by a diagonal matrix with respect to some basis of [tex]V = \mathbb{R}^3[/tex].

So one approach is to use a theorem that says: If T has dim(V) distinct eigenvalues, then T has a diagonal matrix with respect to some basis V.

So we simply look for the solutions of: [tex]\lambda x_1 = 3 x_3[/tex], [tex]\lambda x_2 = 2 x_2[/tex], [tex]\lambda x_3 = x_1[/tex]

So we find that [tex]\lambda = 2, \pm \sqrt 3[/tex] with some corresponding eigenvectors.So we use the theorem and finish off the question.

So finally, my problem with all this. The matrix of T with respect to the standard basis {(0,0,1), (0,1,0),(1,0,0)} (purposely out of order) would be:

[tex]\begin{bmatrix} 3 & 0 & 0 \\ 0 & 2 & 0 \\ 0 & 0 & 1 \end{bmatrix}[/tex]

which is diagonal right? But I have a theorem that says that the eigenvalues of T with respect to a uppertriangular matrix "consist precisely of the entries on the diagonal of that upper-triangular matrix".

But I didn't find 3 and 1 as eigenvalues. Is it because I switched the standard basis elements out of order? Why would that matter? And doesn't this last theorem imply that every linear operator precisely has dim V eigenvalues :S?

It was a long one but any comments are appreciated!
 
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  • #2
bjgawp said:
So finally, my problem with all this. The matrix of T with respect to the standard basis {(0,0,1), (0,1,0),(1,0,0)} (purposely out of order) would be:

[tex]\begin{bmatrix} 3 & 0 & 0 \\ 0 & 2 & 0 \\ 0 & 0 & 1 \end{bmatrix}[/tex]
This is wrong. If we call the basis vectors [itex]v_1,v_2,v_3[/itex] (in the order you've written them), we have for example

[tex]T_{31}=(Tv_1)_3=(3v_3)_3=3(v_3)_3=3\cdot 1=3[/tex]

The third component of [itex]v_3[/itex] in the basis you have defined is 1, not 0, because [itex]v_3=0\cdot v_1+0\cdot v_2+1\cdot v_3[/itex].
 
  • #3
bjgawp said:
So I have a couple of questions in regards to linear operators and their eigenvalues and how it relates to their matrices with respect to some basis.

For example, I want to show that given a linear operator T such that [tex]T(x_1,x_2,x_3) = (3x_3, 2x_2, x_1)[/tex] then T can be represented by a diagonal matrix with respect to some basis of [tex]V = \mathbb{R}^3[/tex].

So one approach is to use a theorem that says: If T has dim(V) distinct eigenvalues, then T has a diagonal matrix with respect to some basis V.

So we simply look for the solutions of: [tex]\lambda x_1 = 3 x_3[/tex], [tex]\lambda x_2 = 2 x_2[/tex], [tex]\lambda x_3 = x_1[/tex]

So we find that [tex]\lambda = 2, \pm \sqrt 3[/tex] with some corresponding eigenvectors.So we use the theorem and finish off the question.

So finally, my problem with all this. The matrix of T with respect to the standard basis {(0,0,1), (0,1,0),(1,0,0)} (purposely out of order) would be:

[tex]\begin{bmatrix} 3 & 0 & 0 \\ 0 & 2 & 0 \\ 0 & 0 & 1 \end{bmatrix}[/tex]
No, if you use that order in the "domain", you must also use it in the "range".
T(0, 0, 1)= (3, 0, 0)= 0(0, 0, 1)+ 0(0, 1, 0)+ 3(1, 0, 0) so the first column will be
[tex]\begin{bmatrix}0 \\ 0 \\ 3\end{bmatrix}[/tex]
T(1, 0, 0)= (0, 0, 1)= 1(0, 0, 1)+ 0(0, 1, 0)+ 0(1, 0, 0) so the third column will be
[tex]\begin{bmatrix} 1 \\ 0 \\ 0\end{bmatrix}[/tex]

In this basis, the matrix corresponding to T is
[tex]\begin{bmatrix}0 & 0 & 1 \\0 & 2 & 0\\ 3 & 0 & 0\end{bmatrix}[/tex]

which is diagonal right? But I have a theorem that says that the eigenvalues of T with respect to a uppertriangular matrix "consist precisely of the entries on the diagonal of that upper-triangular matrix".

But I didn't find 3 and 1 as eigenvalues. Is it because I switched the standard basis elements out of order? Why would that matter? And doesn't this last theorem imply that every linear operator precisely has dim V eigenvalues :S?

It was a long one but any comments are appreciated!
 
  • #4
Ah what a silly mistake I forgot to write each vector as a linear combination of the basis elements.

But then I'm still confused about the whole eigenvalue issue. I know I must be misinterpreting the theorem. The elements along the diagonal are "precisely" the eigenvalues of T. Doesn't this mean that all linear operators have exactly dimV eigenvalues? (Obviously not but I don't see why..)

Thanks a lot!
 
Last edited:
  • #5
The elements along the main diagonal of a diagonal matrix or a "Jordan Normal form" for a non-diagonalizable matrix are the eigenvalues of the matrix but in general, it is much harder to find the eigenvalues of a matrix than just looking at the main diagonal!

It is, however, true that allowing for multiplicities and allowing complex numbers, that any n by n matrix has n eigenvalues- because its eigenvalue equation is an nth degee polynomial which can be factored into n linear factors over the complex numbers.

An n by n matrix may not have n independent eigenvectors.
 
  • #6
Ah right. The entries along the diagonal of a upper triangular matrix need not be distinct.

So then for example: [tex]T(x_1, x_2, x_3) = (2x_1, 2x_2, 3x_3)[/tex]

We only have 2 eigenvalues here but with respect to the standard basis, we would still have a diagonal matrix just with repeated elements along the diagonal. Right?

That clears things up a lot more. Thanks!
 

What is a linear operator?

A linear operator is a mathematical function that maps one vector space to another, and preserves the operations of vector addition and scalar multiplication. It can be represented by a matrix, and is an essential concept in linear algebra.

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are a pair of related concepts in linear algebra. Eigenvalues are scalars that represent the scaling factor of an eigenvector when it is transformed by a linear operator. Eigenvectors are non-zero vectors that are only scaled by a linear operator, and they are associated with their corresponding eigenvalues.

How are diagonal matrices related to eigenvalues?

Diagonal matrices are a special type of matrix where all the entries outside of the main diagonal are zero. They are closely related to eigenvalues because the eigenvalues of a diagonal matrix are simply the entries along the main diagonal. This means that diagonal matrices are particularly useful for solving problems involving eigenvalues.

Can every linear operator be represented by a diagonal matrix?

No, not every linear operator can be represented by a diagonal matrix. This is because diagonal matrices only represent linear operators that have a set of eigenvectors that form a basis for the vector space. If a linear operator does not have a complete set of eigenvectors, it cannot be represented by a diagonal matrix.

How are diagonal matrices used in practical applications?

Diagonal matrices are often used in practical applications because they are easy to manipulate and operate on. For example, in the field of signal processing, diagonal matrices are used to represent filters and perform operations on signals. They are also used in solving systems of linear equations and finding the eigenvalues and eigenvectors of a matrix.

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