Eigenvalue/Eigenvector problem. Check my solution please.

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In summary, the conversation discusses the concept of diagonalizability and Jordan normal form in the context of a matrix with the property A^2=A. The first part shows that if λ is an eigenvalue of A, then λ=0 or λ=1, while the second part proves that A is diagonalizable. The conversation also briefly touches on the relationship between eigenvalues and eigenvectors, as well as the Jordan normal form for n x n matrices.
  • #1
foxofdesert
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Homework Statement


Suppose A is an n x n matrix with the property that A2=A
a. Show that if λ is an eigenvalue of A, then λ=0 or λ=1
b. Prove that A is diagonalizable.

Homework Equations


Av=λv (v : eigenvector)


The Attempt at a Solution



solution for a.
A2v=A(Av)=A(λv)=λ(Av)=λλv. also, Av=λv, therefore, λ2v=λv
=> (λ2-λ)v=0. So, λ=0 or λ=1.


I want you to check if this sounds right. If you see any errors, let me know. Now I'm working on the b part, but I am pretty much stuck. I will post up when I find something. Thanks in advance
 
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  • #2
Looks good so far!
 
  • #3
Thanks for checking.

I've been trying the second part, but have not find any satisfying answers.
from the question, it looks like A is diagonalizable.

But A is n-dimensional, but has only 2 distinct eigenvalues. then, A is not diagonaliable. am I right?
 
  • #4
No, that's not true. The number of eigenvalues is irrelevant. What is relevant is the number of (independent) eigenvectors.

A linear transformation, A, from V to itself, is "diagonalizable" if and only if there exist a basis for V consisting of eigenvectors of A. For an n by n matrix, that means if and only if there exist n independent eigenvectors.

I don't know whether you can use this or not but if A is not diagonalizable, it can be put in Jordan Normal Form- there exist a part of the matrix of the form
[tex]\begin{array}{cc} a & 1 \\ 0 & a\end{array}[/tex]
where a is either 0 or 1 and they are on the diagonal.

Show that [itex]A^2[/itex] cannot have that form.

You should be able to show that the corresponding place on [itex]A^2[/itex] will be
[tex]\begin{array}{cc} a^2 & 2a \\ 0 & a^2\end{array}[/tex]
contradicting [itex]A^2= A[/itex].
 
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  • #5
Thanks HallsofIvy.

I've already submited the homework, but I'm just curious now.

the matrix you showed above is 2 x 2.

what will be Jordan Normal Form in n x n matrix?

upper triangle with 1s?
 
  • #6
foxofdesert said:
Thanks HallsofIvy.

I've already submited the homework, but I'm just curious now.

the matrix you showed above is 2 x 2.

what will be Jordan Normal Form in n x n matrix?

upper triangle with 1s?

If J is the Jordan form then A = P^(-1)JP, and J = PAP^(-1). Thus J^2 = PAP^(-1)PAP(-1) = PA^2P^(-1) = PAP^(-1) [because A^2=A], so J^2 = J. The only type of Jordan form in which this can happen is the diagonal one.

RGV
 
  • #7
foxofdesert said:
But A is n-dimensional, but has only 2 distinct eigenvalues. then, A is not diagonaliable. am I right?
As HallsofIvy said, this is not true. For example, the matrix
\begin{pmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 2 \end{pmatrix}has only two distinct eigenvalues, but you can find three independent eigenvectors, e.g. (1,0,0), (0,1,0), and (0,0,1). And obviously, it's diagonalizable because it's already diagonal.

Jordan normal form looks like, for example,
\begin{pmatrix} \lambda_1 & 1 & 0 & 0 \\ 0 & \lambda_1 & 1 & 0 \\ 0 & 0 & \lambda_1 & 0 \\ 0 & 0 & 0 & \lambda_2 \end{pmatrix}In each block corresponding to an eigenvalue, you have 1s just above the diagonal.
 
  • #8
Thank you for your replies.

I have never studied the jordan normal form (at least not in my class.)

But it looks like it has some role in eigenvalue/vector subject.

Time to dig in!
 

FAQ: Eigenvalue/Eigenvector problem. Check my solution please.

What is the Eigenvalue/Eigenvector problem?

The Eigenvalue/Eigenvector problem is a fundamental concept in linear algebra that involves finding special vectors and their corresponding values in a given matrix. These special vectors are called eigenvectors and the corresponding values are called eigenvalues. This problem has many practical applications in fields such as physics, engineering, and data analysis.

How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors for a given matrix, you first need to find the characteristic polynomial of the matrix. Then, you can solve the characteristic polynomial to find the eigenvalues. Once you have the eigenvalues, you can find the corresponding eigenvectors by solving the system of equations formed by the matrix and the eigenvalues.

What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are significant because they represent the special directions and magnitudes of a matrix. Eigenvectors are often used to transform a matrix into a simpler form, making it easier to analyze and understand. Eigenvalues also have important physical interpretations, such as representing the natural frequencies of a vibrating system.

Can a matrix have more than one eigenvector for the same eigenvalue?

Yes, a matrix can have multiple eigenvectors for the same eigenvalue. In fact, if a matrix has n distinct eigenvalues, it will have n linearly independent eigenvectors. This means that a matrix can have more than one eigenvector for the same eigenvalue, as long as the eigenvectors are not linearly dependent on each other.

What are some real-world applications of the Eigenvalue/Eigenvector problem?

The Eigenvalue/Eigenvector problem has many real-world applications, such as in physics for analyzing vibrating systems and quantum mechanics. It is also used in engineering for analyzing structures and in data analysis for principal component analysis. Additionally, the concept of eigenvectors is used in image processing for tasks such as image compression and denoising.

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