Lin Alg - Eigenvector Existence proof

In summary, the matrix A always has an eigenvector in \mathbb{R}^2, and the eigenvector can be found by considering the geometric interpretation of A or by picking a vector (x 1)T and finding x such that f(x, \theta ) = x and g(x, \theta ) = 1.
  • #1
mattmns
1,128
6
Lin Alg - Eigenvector Existence proof (More of a proof ?, than eigenvector ?)

Here is the question from my book:

Show that If [itex]\theta \in \mathbb{R}[/itex], then the matrix

[tex]A = \left(\begin{array}{cc}cos \theta & sin \theta \\ sin \theta & -cos \theta \end{array}\right)[/tex]

always has an eigenvector in [itex]\mathbb{R}^2[/itex], and in fact that there exists a vector [itex]v_{1}[/itex] such that [itex]Av_{1} = v_{1}[/itex]
---------------

Now I am a little caught up on one thing here:

Is this saying that for any value of [itex]\theta \in \mathbb{R}[/itex], A has an eigenvector in [itex]\mathbb{R}^2[/itex] ?? (imo it does)

Because I am working a simple proof by contradiction, and if I can just choose a value of [itex]\theta[/itex] I would be done. (Except for the second part, but imo, that is simple to do, as I would just need to show that there is some eigenvalue = 1.)

Thanks!
 
Last edited:
Physics news on Phys.org
  • #2
I guess I should be more specific in what I am doing.

I am letting theta be in R, then I am assuming that A has no eigenvectors. Thus for all [itex] X \in \mathbb{R}^2 AX \neq \lambda X [/itex] for any [itex]\lambda[/itex] Then I am letting X = t(0 1) that t is the transpose.
Then I get [itex]AX = t(sin \theta \ \ cos\theta) [/itex]
and [itex]\lambda X = t( 0 \ \ \lambda )[/itex]
Now if [itex]\theta[/itex] takes on certain values, then [itex]AX = \lambda X[/itex] which is a contradiction (that is, if theta can be any value in R)
 
Last edited:
  • #3
Your approach is wrong. You must show the existence of eigenvectors for all values of theta, not just certain values of theta.

Since you have to solve the eigenvalue equation, why not just go a step further an find the eigenvectors (it's not long at all) ? If you get stuck along the way, someone will bail you out.
 
  • #4
You're supposed to prove that for ALL [itex]\theta[/itex] in [itex]\mathbb{R}[/itex], the corresponding matrix A has an eigenvector; you've only shown that only for SOME [itex]\theta \in \mathbb{R}[/itex], the corresponding matrix A has an eigenvector.

Do you know the geometric interpretation of these matrices A? If so, then the whole thing would be easy, and you'd know what eigenvector to look for. Try investigating. Look at the eight points on the unit circle that are at some multiple of 45 degrees. Now consider the matrices you get when you take [itex]\theta[/itex] to be some multiple of 45 degrees.

Or here's a way to almost always find an eigenvector: Pick a vector (x 1)T. Apply the matrix A to it, and you'll get a resulting vector:

[tex]\left (\begin{array}{c}{f(x,\theta )\\g(x,\theta )}\end{array}\right )[/tex]

For some functions f and g. Remember you're asked to find, given the angle [itex]\theta[/itex] an eigenvector, so you want to find x such that [itex]f(x, \theta ) = x[/itex], [itex]g(x, \theta ) = 1[/itex] since you already expect the eigenvalue to be 1. This procedure will only fail when [itex]\theta[/itex] is an integer multiple of 360 degrees.
 
  • #5
I had first approached it that way, but I could just not seem to get anywhere, now I got it. Thanks!
 
  • #6
Now that you've figured out the answer for yourself, you should know that this matrix gives a reflection in the line through the origin which makes an angle [itex]\theta / 2[/itex] with the positive x-axis. Any vector in this line is obviously an eigenvector with eigenvalue 1.
 

FAQ: Lin Alg - Eigenvector Existence proof

What is an eigenvector?

An eigenvector is a vector that, when multiplied by a given matrix, remains in the same direction but may be scaled by a scalar value. In other words, the matrix only changes the magnitude of the vector, not its direction.

How do you prove the existence of eigenvectors?

The existence of eigenvectors can be proved by solving the characteristic equation for the given matrix. The solutions to this equation represent the eigenvalues, and the corresponding eigenvectors can be found by plugging each eigenvalue into the original matrix and solving for the vector that satisfies the equation Ax = λx.

Is it possible for a matrix to have no eigenvectors?

Yes, it is possible for a matrix to have no eigenvectors. This occurs when the characteristic equation has no real solutions, meaning there are no eigenvalues. This can happen, for example, when the determinant of the matrix is equal to zero.

Can a matrix have multiple eigenvectors for the same eigenvalue?

Yes, a matrix can have multiple eigenvectors for the same eigenvalue. In fact, it is common for matrices to have multiple eigenvectors for each eigenvalue. This means that there are multiple possible directions for the vector to be scaled by the scalar value without changing its direction.

How are eigenvectors and eigenvalues used in real-world applications?

Eigenvectors and eigenvalues are commonly used in data analysis and machine learning, particularly in the fields of image and signal processing. They are also used in physics and engineering to solve systems of differential equations and to study the behavior of dynamical systems.

Back
Top