Find Eigenspace for Matrix A with 2 Eigenvalues

In summary, your matrix has two real eigenvalues, 3 and 0. The eigenspace corresponding to 3 is two dimensional and is spanned by [0, 0, 1]T. The eigenspace corresponding to 0 is one dimensional and is spanned by [0, 0, 1]T.
  • #1
UrbanXrisis
1,196
1
matrix [tex]A = \left(\begin{array}{ccc}3&0&0 \\ 0&3&0 \\3&0&0 \end{array}\right)[/tex]

has two real eigenvalues lambda_1=3 of multiplicity 2, and lambda_2=0 of multiplicity 1. find the eigenspace.

[tex]A = \left(\begin{array}{ccc}3-3 &0&0 \\ 0&3-3&0 \\3&0&0-3 \end{array}\right)[/tex]

[tex]A = \left(\begin{array}{ccc}0 &0&0 \\ 0&0&0 \\3&0&-3 \end{array}\right)[/tex]

[tex]A = \left(\begin{array}{ccc}1&0&-1 \\ 0&0&0 \\0&0&0 \end{array}\right)[/tex]

that means the eigenspace for lambda_1 is the column vector [0 1 0] and [1 0 1]

for lambda_2, there will be two different eigenspaces such that...

[tex]A = \left(\begin{array}{ccc}1&0&0 \\ 0&1&0 \\0&0&0 \end{array}\right)[/tex]

I know the answer has two vectors, how do i get two vectors when I have 4 that defines the eigenspace?
 
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  • #2
Looks correct up to here ...
UrbanXrisis said:
for lambda_2, there will be two different eigenspaces such that...
How do you get that ?
 
  • #3
Is your eigenvector for [tex]\lambda_3[/tex] not [tex]v_3=(0, 0, k)^T,\;k\in\mathbb{R}[/tex], and for [tex]\lambda_{1,2}[/tex] not [tex]v_{1,2}=(k_1,k_2,k_1)^T,\;k_{1,2}\in\matbb{R}[/tex]?
 
  • #4
UrbanXrisis said:
for lambda_2, there will be two different eigenspaces such that...

[tex]A = \left(\begin{array}{ccc}1&0&0 \\ 0&1&0 \\0&0&0 \end{array}\right)[/tex]

I know the answer has two vectors, how do i get two vectors when I have 4 that defines the eigenspace?
If
[tex]A = \left(\begin{array}{ccc}1&0&0 \\ 0&1&0 \\0&0&0 \end{array}\right)\left(\begin{array}{c}x \\y\\ z\end{array}\right)= \left(\begin{array}{c}0 \\ 0 \\0 \end{array}\right)[/tex]
Which says x= 0, y= 0. Looks to me like you have just a one dimensional eigen space, spanned by (0, 0, 1)T.
 
  • #5
oh right, so three vectors then... [0, 0, 1], [0 1 0] and [1 0 1]

the solution gives 2 vectors, how would I find the eigenspace from 3 vectors?
 
  • #6
UrbanXrisis said:
oh right, so three vectors then... [0, 0, 1], [0 1 0] and [1 0 1]

the solution gives 2 vectors, how would I find the eigenspace from 3 vectors?

WHAT solution gives 2 vectors and what eigenspace are you talking about?

Not all texts use that terminology but I think by "eigenspace" you mean the sub-space consisting of eigenvectors haveing the same eigenvalue. In this case, you have two distinct "eigenspaces", one having dimension 2 and the other dimension 1.
 
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  • #7
Depending on the definition, I think you could also use the general eigenvectors that I gave and form your basis by sticking in ones and zeroes.
 
  • #8
HallsofIvy said:
If
[tex]A = \left(\begin{array}{ccc}1&0&0 \\ 0&1&0 \\0&0&0 \end{array}\right)\left(\begin{array}{c}x \\y\\ z\end{array}\right)= \left(\begin{array}{c}0 \\ 0 \\0 \end{array}\right)[/tex]
Which says x= 0, y= 0. Looks to me like you have just a one dimensional eigen space, spanned by (0, 0, 1)T.


yeah right.

If you start with a three dimensional square hermitian matrix, you'll get a 3D eigenspace. period. If you have degenerate eigenvalues - like you do, then you get one unambiguous eigenvector (for the eigenvalue 0) and a 2D eigenspace (corresponding to the degenerate eigenvalue 1) from which you have the freedom of choosing any two linearly independent eigenvectors.
 
  • #9
That's not the question I was responding to.
(And, the matrix we are talking about is not Hermitian.)
 
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  • #10
so... there are three vectors the span the space... this is because:

[tex]\lambda_1=3[/tex]

[tex]A = \left(\begin{array}{ccc}1&0&-1 \\ 0&0&0 \\0&0&0 \end{array}\right)[/tex]

[tex] \left(\begin{array}{c}a \\b \\c \end{array}\right) = s \left(\begin{array}{c}0 \\1 \\0 \end{array}\right) + t \left(\begin{array}{c}1 \\0 \\1 \end{array}\right) [/tex]

[tex]\lambda_1=0[/tex]

[tex]A = \left(\begin{array}{ccc}1&0&0 \\ 0&1&0 \\0&0&0 \end{array}\right)[/tex]

[tex] \left(\begin{array}{c}a \\b \\c \end{array}\right) = u \left(\begin{array}{c}0 \\0 \\1 \end{array}\right) [/tex]

so, there are three linearly independent vetors that span the eigenspace

they are: [tex] \left(\begin{array}{c}0 \\0 \\1 \end{array}\right) , \left(\begin{array}{c}0 \\1 \\0 \end{array}\right) , \left(\begin{array}{c}1 \\0 \\1 \end{array}\right)[/tex]

however, my solution is telling me that I need two vectors instead of three such that:
[tex] \left(\begin{array}{c}a \\b \\c \end{array}\right) , \left(\begin{array}{c}d \\e \\f \end{array}\right) [/tex]
 
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  • #11
Then what was the question? Yes, the three vectors span the space- it is, after all, 3 dimensional.

"my solution is telling me that I need two vectors". Your solution? What solution are you talking about? Your matrix has two distinct eigenvalues, 3 and 0. The eigenspace corresponding to 3 is two dimensional and is spanned by
[tex] \left(\begin{array}{c}1 \\0 \\1 \end{array}\right)[/tex]
and
[tex]\left(\begin{array}{c}0 \\1 \\0 \end{array}\right) [/tex]
The eigenspace corresponding to 0 is one dimensional and is spanned by
[tex]\left(\begin{array}{c}0 \\0 \\1 \end{array}\right)[/tex]

Those three vectors, of course, span all of R3.
 
  • #12
oh whoops, I was to find the eigenspace for only [tex]\lambda_1=3[/tex]

so I didnt have to take into account [tex]\lambda_2[/tex]
 

1. What is an eigenspace?

An eigenspace is a vector space associated with a specific eigenvalue of a matrix. It consists of all the vectors that when multiplied by the matrix, result in a scalar multiple of the same vector.

2. How many eigenspaces can a matrix have?

A matrix can have as many eigenspaces as the number of distinct eigenvalues it has. Each eigenspace corresponds to one eigenvalue.

3. How do you find the eigenspace for a specific eigenvalue?

To find the eigenspace for an eigenvalue, you first need to find the corresponding eigenvectors by solving the characteristic equation. Then, the eigenspace will consist of all the linear combinations of these eigenvectors.

4. Can a matrix have more than one eigenspace for the same eigenvalue?

Yes, a matrix can have multiple eigenspaces for the same eigenvalue. This happens when the eigenvectors corresponding to the eigenvalue are not linearly independent, meaning they are scalar multiples of each other.

5. Why is finding eigenspaces important?

Finding eigenspaces is important because it allows us to understand the behavior of a matrix when multiplied by different vectors. Eigenspaces also help in solving systems of linear equations and in understanding the geometric transformations represented by a matrix.

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