Eigenvalues, eigenvectors, and eigenspaces

In summary: T If by behave as expected you mean give back the zero vector, then yes (after I made an adjustment to the second eigenvector calculation). If this is the expected result, why do I expect this matrix multiplication to give back a column of all zeros?
  • #1
TranscendArcu
285
0

Homework Statement


Screen_shot_2012_02_26_at_3_59_09_PM.png

The Attempt at a Solution


T(1,0,0) = (3,-1,0)
T(0,1,0) = (0,1,0)
T(0,0,1) = (-1,2,4)

Thus, we have the matrix,

[itex]\left| \begin{array}{ccc}
3 &0&-1 \\
-1&1&2 \\
0&0&4 \end{array} \right|[/itex]

[itex]Δ_T (t) = det( \left| \begin{array}{ccc}
3 &0&-1 \\
-1&1&2 \\
0&0&4 \end{array} \right| - tI)[/itex]

I have this equaling: -(t-4)(t-3)(t-1), which is the characteristic polynomial. The roots are the eigenvalues, which are 4,3,1.

To compute the eigenvectors:

When t=4, we have,
-x-z=0
-x-3y+2z=0
0z=0

Which implies that eigenvectors are multiples of (-1,1,1).

When t=3, we have,
-z=0
-x-2y=0
z=0

Which implies that eigenvectors are multiples of (-1,2,0)

When t=1, we have,
2x-z=0
-x+2z=0
3z=0

Which implies that eigenvectors are multiples of (0,1,0).

T is diagonizable because (-1,1,1),(-1,2,0),(0,1,0) are lin. indep.

First of all, are these correct? Also, how does one determine eigenspaces? That's the part I feel that I really don't know.
 
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  • #2
to see if they're correct, test by multiplying your eignevectors by the matrix - do they behave as expected?
 
  • #3
as for the eigenspaces, they will be the span of all eignevectors corresponding to a single eigenvalue

what does the span of a single vector look like?
 
  • #4
Eigenspace are the eigenvectors. I obtained a different eigenvector for you second one. I don't believe I made a mistake but I could have.
 
  • #5
fauboca said:
Eigenspace are the eigenvectors. I obtained a different eigenvector for you second one. I don't believe I made a mistake but I could have.

Okay. I have the equations

-z=0
-x-2y=0
z=0

So clearly z=0 and x=-2y. Thus, is the eigenvector multiples of (1,1/-2,0)? Or, equivalently, multiples of (-2,1,0)?

lanedance said:
as for the eigenspaces, they will be the span of all eignevectors corresponding to a single eigenvalue

what does the span of a single vector look like?

So the span of a single vector is just a line, no? So, for example, considering the eigenvalue of 4, is the eigenspace just: span(-1,1,1)?

For eigenvalue of 1: span(0,1,0)?

lanedance said:
to see if they're correct, test by multiplying your eignevectors by the matrix - do they behave as expected?

If by behave as expected you mean give back the zero vector, then yes (after I made an adjustment to the second eigenvector calculation). If this is the expected result, why do I expect this matrix multiplication to give back a column of all zeros?
 
  • #6
TranscendArcu said:
So the span of a single vector is just a line, no? So, for example, considering the eigenvalue of 4, is the eigenspace just: span(-1,1,1)?

For eigenvalue of 1: span(0,1,0)?

The line is correct, you could parameterise to describe it if need be

TranscendArcu said:
If by behave as expected you mean give back the zero vector, then yes (after I made an adjustment to the second eigenvector calculation). If this is the expected result, why do I expect this matrix multiplication to give back a column of all zeros?

Are you multiplying by [itex] T [/itex] or [itex] (T - \mathbb{I} \lambda)[/itex]?

Say your eignevector is [itex] u [/itex] multply by [itex] T [/itex] you should get the original vector multiplied by the corresponding eigenvalue [itex] Tu =\lambda_u u[/itex]
 

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to analyze the behavior of a linear transformation or a matrix. Eigenvalues are the possible scaling factors of the eigenvectors, which are special vectors that remain in the same direction after being multiplied by a matrix.

How are eigenvalues and eigenvectors calculated?

To find the eigenvalues and eigenvectors of a matrix, we first need to solve the characteristic equation, which is given by det(A - λI) = 0, where A is the matrix and λ is the eigenvalue. The resulting eigenvalues can then be used to find the corresponding eigenvectors by solving the equation (A - λI)v = 0.

What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many applications in various fields, including physics, engineering, and computer science. They are used to understand the behavior of systems and to solve differential equations. In computer graphics, they are used to rotate and scale objects, and in data analysis, they are used to reduce the dimensions of a dataset.

What is an eigenspace?

An eigenspace is a vector space that is spanned by the eigenvectors of a matrix corresponding to a specific eigenvalue. It is a subspace of the original vector space and contains all the possible linear combinations of the eigenvectors. Eigenspaces are useful for understanding the different behaviors of a matrix or a system.

How are eigenvalues and eigenvectors used in diagonalization?

Diagonalization is a process of finding a diagonal matrix that is similar to a given matrix. Eigenvalues and eigenvectors are crucial in this process as they are used to construct the diagonal matrix by rearranging the eigenvectors and placing the corresponding eigenvalues on the diagonal. Diagonalization is useful for simplifying calculations and solving systems of linear equations.

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