Finding the eigenvector normalization for a 2×2 matrix

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Homework Statement


Printed textbook page on eigenspaces: case lambda = 2, with 2I minus A equal to a 2 by 2 matrix [-2 -2; 1 1], rank 1, and the eigenvector u1 = [1 -1]^T, followed by the product A u1 = 2 u1 worked out with column vectors.

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]
 
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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!)
 
g.lemaitre said:

Homework Statement



Textbook page: Case λ = 2, finding a basis for the null space of 2I − A = [[-2, -2], [1, 1]]. Text states rank 1, dim NS = 1, u1 = [1, -1]^T, and the check A u1 = 2 u1 shown with matrix products.


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 <br /> 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}<br /> [/tex]
which is

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

I pretty much cleaned it up for you.
 
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.
 
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:
Textbook page, Example 6: finding bases for the eigenspaces of the matrix A = [4 2; -1 1], with its characteristic equation (λ-4)(λ-1)+2 = (λ-2)(λ-3) and the matrices λI - A for λ = 2 or 3.

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

Handwritten textbook solution for eigenspaces with lambda = 6: matrix 6I - A reduced to echelon form U with rows [4 -3 0], [0 0 0], [0 0 0]; basis vectors u2 = [3/4 1 0]^T and u3 = [0 0 1]^T.


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.
 
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.