Finding Eigenvalues and Bases of Eigenspace for a Given Matrix

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In summary, the matrix A has one real eigenvalue of 0. I found the first basis but I can't seem to figure out how to come up with the second.
  • #1
ihumayun
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Homework Statement



The matrix A=
2 0 4
-2 0 -4
-1 0 -2

has one real eigenvalue. Find this eigenvalue and a basis of the eigenspace.

Eigenvalue =

Basis ( , , )T , ( , , )T


Homework Equations



The Attempt at a Solution



I have found the eigenvalue to be 0, but I can't seem to figure out how to come up with 2 bases. I found the first one:

2x + 4z = 0
-2x - 4z = 0
-1x - 1z = 0

All three equations reduce to :
x + 2z = 0

So then,

x = t , y = 0, and z = -(1/2)t
which gives a basis of (1, 0, -1/2)T

I'm not completely sure that I have calculated this correctly, and I have no idea how to come up with the second basis. The eigenvalue I calculated was marked correct, though. If anyone could help me, I would greatly appreciate it. :)
 
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  • #2
You only have one eigenvalue so you will have only one basis.

Your question states "Find this eigenvalue and a basis of the eigenspace".

Correct me if I am wrong but you should only need to find one basis.

Thanks
Matt
 
  • #3
ihumayun said:
All three equations reduce to :
x + 2z = 0

So then,

x = t , y = 0, and z = -(1/2)t
Could you show your work here?
 
  • #4
I also thought that there should be only one basis, but the question asks for 2, and my prof confirmed this. I still don't understand how this would work though.

My work:
x+2z=0
2z = -x
z = - x/2

So if I set x with the parameter t,

x=t
y=0
and z = -t/2, or (-1/2) t

Sorry I didn't include this before .
 
  • #5
ihumayun said:
z = - x/2

So if I set x with the parameter t,

x=t
y=0
and z = -t/2
y doesn't appear in that equation -- how did you conclude something about it?
 
  • #6
Because the equation was

x +2z = 0 or
1x + 0y +2z = 0

y is nonexistent in this case. I assumed that would mean it would be equal to 0 in the basis, but like I said, I wasn't sure if I had calculated the basis correctly. How would I find a value for y?
 
  • #7
All equations reduce to x+ 2z= 0 so x= -2z. z can be anything at all and y can be anything at all. Your basis contains two vectors.
 
  • #8
So how would I find the bases in that case?
 
  • #9
Oh, I just got it.
x = -2t
y = s, not 0
and z = t

which means that the vector would be:

(-2t , s, t)T = (-2t, 0, t)T + ( 0, s, 0)T

which gives the bases

(-2, 0, 1)T, and (0, 1, 0)T
 
  • #10
Thank you for your help!
 

What is the basis of the eigenspace?

The basis of the eigenspace refers to a set of eigenvectors that form a basis for the eigenspace of a linear transformation or matrix. These eigenvectors are associated with a specific eigenvalue and can be used to represent the transformation or matrix in a more simplified form.

Why is the basis of the eigenspace important?

The basis of the eigenspace is important because it allows us to understand the behavior of a linear transformation or matrix in a more structured way. It also helps in solving systems of linear equations and finding solutions to differential equations.

How is the basis of the eigenspace calculated?

The basis of the eigenspace can be calculated by finding the eigenvectors of the linear transformation or matrix and then checking if they form a basis. This can be done through various methods such as diagonalization, Jordan canonical form, or using the characteristic polynomial.

Can the basis of the eigenspace change?

Yes, the basis of the eigenspace can change if the linear transformation or matrix is altered. For example, if the eigenvalues or eigenvectors are changed, the basis of the eigenspace will also change. However, the dimension of the eigenspace will remain the same.

What is the relationship between the basis of the eigenspace and the eigenvalues?

The basis of the eigenspace is directly related to the eigenvalues of a linear transformation or matrix. Each eigenvector in the basis corresponds to a specific eigenvalue. Additionally, the number of eigenvectors in the basis is equal to the multiplicity of the associated eigenvalue.

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