How can I find the eigenvectors and basis for the eigenspace of a given matrix?

In summary, the conversation discusses finding the eigenvectors and basis for the eigenspace of a given matrix. The solution involves solving equations for a specific eigenvalue, with a small error corrected later. The eigenvectors for both eigenvalues can be used as a basis for the eigenspace. Normalizing the vectors is not necessary, but can be done if desired.
  • #1
jjones1573
22
0

Homework Statement


Im looking at finding the eigenvectors of a matrix but also a basis for the eigenspace

A = [ 6 16 ]
[ -1 -4 ]

lambda = 4
lambda = -2


Homework Equations


(A - lambda I ) v = 0


The Attempt at a Solution



So with the above equation I get:

for lambda = 4

[ 6 - 4 16 ] [ v1 ] = [ 0 ]
[ -1 -4 - 4 ] [ v2 ] [ 0 ]

so

2 v1 + 16 v2 = 0
-v1 - 8v2 = 0

so v1 = 8v2

and the basis for the eigenspace is span [ 8 ]
[ 1 ]

First is that right? because when I put it into an eigenvector calculator on the web it gives me
-8 instead of 8 but I can't see how I could get to that.

Second if this is the basis for the eigenspace then how can I find the eigenvectors for the eigenvalue?

thanks,
 
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  • #2
jjones1573 said:
2 v1 + 16 v2 = 0
-v1 - 8v2 = 0

so v1 = 8v2

You made a sign error. v1=-8v2

ehild
 
  • #3
Oh yeah that's right thanks.

Is it as simple as the vector is:

[-8v2]
[v2]

and the eigenspace is: span

[-8]
[1]
 
  • #4
Sorry I realized this should have been posted in the calculus section would it be possible to have it moved?

I think what I have put above for the eigenspace is correct? But what about the eigenvector I can't seem to understand what this is.
 
  • #5
(-8,1) multiplied by any number is an eigenvector. You need to find the other one, which belongs to the other eigenvalue lambda=2.
The two eigenvectors are the basis of the "eigenspace". You can choose the normalised vectors as basis.

ehild
 
  • #6
Oh thanks. Do I need to normalise the vectors or is it fine to just find the two vectors and give that?
 
  • #7
You do not need to normalize in principle.

ehild
 
  • #8
ok thanks.
 

1. What is an eigenvector?

An eigenvector is a vector that, when multiplied by a given matrix, results in a scalar multiple of itself. In other words, the direction of the eigenvector is not changed by the transformation of the matrix, but its length may be scaled.

2. What is an eigenspace?

An eigenspace is the set of all eigenvectors corresponding to a particular eigenvalue of a matrix. It can be thought of as the "subspace" of the original vector space that is preserved by the transformation of the matrix.

3. How are eigenvectors and eigenvalues related?

Eigenvectors and eigenvalues are closely related, as an eigenvalue is simply the scalar multiple that results when an eigenvector is multiplied by a matrix. In other words, an eigenvector and its corresponding eigenvalue are inseparable.

4. How are eigenvectors and eigenspaces used in linear algebra?

Eigenvectors and eigenspaces are important concepts in linear algebra because they allow for the simplification and analysis of complex systems. They are often used to find the most important directions or patterns in a dataset, or to solve systems of differential equations.

5. How are eigenvectors and eigenspaces calculated?

Eigenvectors and eigenspaces are calculated by finding the solutions to the characteristic equation of a matrix, which involves finding the roots of the characteristic polynomial. This can be done using various methods, such as Gaussian elimination or using specialized algorithms.

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