Eigenspace and basis of eigenvectors

In summary, the conversation discusses finding eigenspaces for a given matrix and proving that there cannot be a basis of R3 consisting entirely of eigenvectors. The correct eigenvalues and eigenvectors are found, and it is determined that the eigenspace includes the zero vector. However, a basis for the eigenspace should be provided. It is then explained that a basis for R3 must contain three linearly independent vectors, which the eigenspaces do not provide. The conversation ends with gratitude for the help provided.
  • #1
Locoism
81
0

Homework Statement


Given the matrix
0 1 0
0 0 1
-3 -7 -5

Find the eigenspaces for the various eigenvalues
Prove that there cannot be a basis of R3 consisting entirely of eigenvectors of A

Homework Equations


The Attempt at a Solution


The eigenvectors are not a problem, I end up with (λ+3)(λ+1)2 so my eigenvalues are -3 and -1. Substituting in I get [1, -3, 9] and [1, -1, 1]. Now would the eigenspace simply be {[1, -3, 9], [1, -1, 1], [0, 0, 0]} or am I missing some other step?

Also, how can I prove there cannot be a basis of R3 consisting of eigenvectors of A? Could it just be because there must be n vectors in a basis of Rn?
 
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  • #2
Locoism said:

Homework Statement


Given the matrix
0 1 0
0 0 0
-3 -7 -5

Find the eigenspaces for the various eigenvalues
Prove that there cannot be a basis of R3 consisting entirely of eigenvectors of A


Homework Equations





The Attempt at a Solution


The eigenvectors are not a problem, I end up with (λ+3)(λ+1)2 so my eigenvalues are -3 and -1. Substituting in I get [1, -3, 9] and [1, -1, 1]. Now would the eigenspace simply be {[1, -3, 9], [1, -1, 1], [0, 0, 0]} or am I missing some other step?
Don't include the zero vector. It can't be a vector in a basis.
Locoism said:
Also, how can I prove there cannot be a basis of R3 consisting of eigenvectors of A? Could it just be because there must be n vectors in a basis of Rn?
For R3, any basis must contain 3 linearly independent vectors. Since your basis contains only two vectors, these two vectors could not possibly span R3, and so aren't a basis for R3.
 
  • #3
Locoism said:

Homework Statement


Given the matrix
0 1 0
0 0 0
-3 -7 -5

Find the eigenspaces for the various eigenvalues
Prove that there cannot be a basis of R3 consisting entirely of eigenvectors of A


Homework Equations





The Attempt at a Solution


The eigenvectors are not a problem, I end up with (λ+3)(λ+1)2 so my eigenvalues are -3 and -1. Substituting in I get [1, -3, 9] and [1, -1, 1]. Now would the eigenspace simply be {[1, -3, 9], [1, -1, 1], [0, 0, 0]} or am I missing some other step?

Also, how can I prove there cannot be a basis of R3 consisting of eigenvectors of A? Could it just be because there must be n vectors in a basis of Rn?

Your characteristic polynomial is wrong. So the eigenvalues and eigenvectors are too. Did you put the correct matrix in the problem statement?
 
  • #4
Dick makes a good point. After putting in all that effort to find eigenvalues and eigenvectors, you should at least check your work. If x is an eigenvector with eigenvalue [itex]\lambda[/itex], then it should be true that Ax = [itex]\lambda[/itex]x.
 
  • #5
Aaahhh sorry, I missed a 1, it's corrected now
 
  • #6
Locoism said:
Aaahhh sorry, I missed a 1, it's corrected now

That's better. Now listen to Mark44 on the basis part of the question.
 
  • #7
Thank you. So I know I can't have the zero vector as part of a basis, but should it be included in the eigenspace? or is the eigenspace juste a basis of the eigenvectors? The terminology confuses me a little.
 
  • #8
The eigenspace is the set of all linear combinations of the basis vectors. The eigenspace is a vector space, which like all vector spaces, includes a zero vector.

No one is asking you to list the eigenspace (an impossible task) - just a basis for it.
 
  • #9
Locoism said:
Thank you. So I know I can't have the zero vector as part of a basis, but should it be included in the eigenspace? or is the eigenspace juste a basis of the eigenvectors? The terminology confuses me a little.

Be careful. You have two different eigenspaces here. One corresponding to the eigenvalue -3 and another to the eigenvalue -1. What's a basis for each?
 
  • #10
Oh ok so basis for lambda=-3 is span(1, -3, 9) and for -1 it is span(1, -1, 1).
Why is it that there can't be a basis for R3 of only eigenvectors?
 
  • #11
Locoism said:
Oh ok so basis for lambda=-3 is span(1, -3, 9) and for -1 it is span(1, -1, 1).
Why is it that there can't be a basis for R3 of only eigenvectors?

It's basically what you said. To span R3 you need three linearly independent eigenvectors. The eigenspaces only give you two.
 
  • #12
Alright thank you so much guys, this was really helpful. Is there some way to +rep or something?
 
  • #13
Locoism said:
Alright thank you so much guys, this was really helpful. Is there some way to +rep or something?

If you mean a ratings boost, no, we don't have that. Thanks is enough. Very welcome.
 
  • #14
Same here. Posters don't always say "thank you," but it's appreciated when they do.
 

1. What is an eigenspace?

An eigenspace is a vector space that is associated with a specific eigenvalue of a linear transformation. It is a set of all eigenvectors corresponding to that eigenvalue, along with the zero vector.

2. How is an eigenspace related to eigenvectors?

Eigenvectors are the basis vectors of an eigenspace. They are the vectors that remain in the same direction after a linear transformation is applied. The eigenspace contains all possible linear combinations of these eigenvectors.

3. What is the significance of eigenspaces in linear algebra?

Eigenspaces are important because they provide a way to decompose a linear transformation into simpler parts. By finding the eigenvectors and eigenvalues of a transformation, we can understand how the transformation affects different directions in space.

4. Can an eigenspace have more than one basis?

Yes, an eigenspace can have multiple bases. This is because there can be multiple linearly independent eigenvectors for a given eigenvalue. However, all bases of an eigenspace will have the same number of vectors, which is the dimension of the eigenspace.

5. How are eigenspaces used in real-world applications?

Eigenspaces have many practical applications, such as in image and signal processing, data compression, and quantum mechanics. They are also used in solving differential equations and in understanding the behavior of complex systems, such as in economics and biology.

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