Understanding the Nullspace of Eigenspaces

  • Thread starter g.lemaitre
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In summary: After reducing it to row echelon form, one of the values = 1. So x - 3/4y = 0. Then you put one of the variables on the other side of the equation, so that u_1 becomes [3/4 1 0]^T. But I would think it should be [1 3/4 0] because that is the value of x after you reduced it to row echelon form.
  • #1
g.lemaitre
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Homework Statement


Screenshot2012-07-28at80229PM.png

Homework Equations


The Attempt at a Solution


I don't understand how [tex]u_1[/tex] = [tex][1 -1]^T [/tex]? By my reckoning [tex]u_1[/tex] =
[tex]
\frac{v_1}{\parallel v_1 \parallel}
[/tex]
which is
[tex]
\frac{-2}{\parallel -2+1 \parallel}, \frac{-2}{\parallel -2+1 \parallel}
[/tex]
which is
[-2, -2] not [1, -1]
 
Last edited:
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  • #2
Could you clear up your tex? It is hard to understand what your actual question is. Use [ tex] and [ /tex] tags for tex. (Without the space!)
 
  • #3
g.lemaitre said:

Homework Statement



Screenshot2012-07-28at80229PM.png


Homework Equations


The Attempt at a Solution



I don't understand how [itex]u_1[/itex] = [itex][1, -1]^T [/itex]?

By my reckoning [itex]\displaystyle
u_1=\frac{v_1}{\parallel v_1 \parallel} [/itex]

which is
[tex]
\frac{-2}{\parallel -2+1 \parallel}, \frac{-2}{\parallel -2+1 \parallel}

[/tex]
which is

[-2, -2] not [1, -1]
There.

I pretty much cleaned it up for you.
 
  • #4
I do not know what v_1 is, as it is not in the problem statement. But I can explain where u_1 comes from. The problem gives the matrix 2I-A. If you take u_1 and calculate (2I-A)u_1, then you get the 0 vector. Thus, u_1 lies in the null space of (2I - A). Since the null space of 2I - A has dimension one, that means that u_1 spans the null space of 2I - A, and so that 2Iu_1 = Au_1, meaning that u_1 is an eigenvector of A, and thus spans the eigenspace of A. Also, you do not have to normalize u_1, since the span of the normalized u_1 is the same as the span of u_1.
 
  • #5
who_ said:
I do not know what v_1 is, as it is not in the problem statement. But I can explain where u_1 comes from. The problem gives the matrix 2I-A. If you take u_1 and calculate (2I-A)u_1, then you get the 0 vector. Thus, u_1 lies in the null space of (2I - A). Since the null space of 2I - A has dimension one, that means that u_1 spans the null space of 2I - A, and so that 2Iu_1 = Au_1, meaning that u_1 is an eigenvector of A, and thus spans the eigenspace of A. Also, you do not have to normalize u_1, since the span of the normalized u_1 is the same as the span of u_1.
Here is more of the question:
Screenshot2012-07-29at35312AM.png

if u_1 spans the null space of 2I - A, then that does not explain how u_1 is [tex][1 -1]^T[/tex]
 
Last edited:
  • #6
I think I understand what's going on now. Here's another example:

Screenshot2012-07-29at50229AM.png


It looks like after you reduce things to row echelon form, you reduce it again so that one of the values = 1. here, x -3/4y = 0. then you put one of the variables on the other side of the equation, so that u_1 becomes [tex][3/4 1 0]^T[/tex]. But I would think it should be [tex][1 3/4 0][/tex]

I'm not sure about u_2 however.
 
  • #7
You're looking for the solution to (2I-I)x=0, right? So solve
$$(2I-A)\vec{x} = \begin{pmatrix} -2 & -2 \\ 1 & 1 \end{pmatrix}\begin{pmatrix} x \\ y \end{pmatrix} = 0.$$ Forget about nullspaces and eigenspaces for the moment. You're just solving a system of equations.
 
  • #8
I sort of understand what is going on.
 

1. What is the nullspace of an eigenspace?

The nullspace of an eigenspace is the set of all vectors that, when multiplied by the eigenvector, result in the zero vector. In other words, it is the subspace of the original vector space where the transformation associated with the eigenvector has no effect.

2. How is the nullspace of an eigenspace related to eigenvectors?

The nullspace of an eigenspace is directly related to eigenvectors because it is the set of vectors that are multiplied by the eigenvector to produce the zero vector. This means that the nullspace is a subset of the eigenspace.

3. Can the nullspace of an eigenspace be empty?

Yes, the nullspace of an eigenspace can be empty. This can happen when there are no eigenvectors associated with a particular eigenvalue. In this case, the nullspace of the eigenspace will be the empty set, as there are no vectors that can be multiplied by the eigenvector to produce the zero vector.

4. How do you find the nullspace of an eigenspace?

To find the nullspace of an eigenspace, you first need to find the eigenvectors associated with the eigenvalue of interest. Then, you can set up a system of equations using the eigenvector components and solve for the nullspace vectors that satisfy the equation Ax=0, where A is the matrix associated with the eigenvector.

5. What is the significance of the nullspace of an eigenspace in linear algebra?

The nullspace of an eigenspace is significant in linear algebra because it helps us understand the behavior of a linear transformation on a vector space. It can also provide insight into the structure and properties of the original vector space. Additionally, the nullspace of an eigenspace is used in solving systems of linear equations and in finding the eigenvalues of a matrix.

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