Eigenvalues for an Invertible Matrix

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Doesy
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Homework Statement


A is an invertible matrix, x is an eigenvector for A with an eiganvalue [tex]\lambda[/tex] [tex]\neq[/tex]0 Show that x is an eigenvector for A^-1 with eigenvalue [tex]\lambda[/tex]^-1


Homework Equations


Ax=[tex]\lambda[/tex]x
(A - I)x

The Attempt at a Solution



I know that I need to find x and then apply to the inverses of my Matrix and eigenvalue, but how do I know what matrix to use for A? Do I use the inverse matrix as it is an invertible matrix? Can I use any invertible matrix to prove this?

Thanks in advance.
 
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Let [itex]\mu[/itex] be the eigenvalue of [itex]A^{-1}[/itex]. Now multiply the eigenvalue equation with [itex]A^{-1}[/itex] and find [itex]\mu[/itex].
 
Do you mean I should have

A[tex]^{2}[/tex]x = A[tex]\lambda[/tex]x

and

A[tex]^{-2}[/tex]x = A[tex]^{-1}[/tex][tex]\mu[/tex]x

?

How can I use this to show my Answer? Or do I substitute this second equation into the first?
 
No with multiplying the eigenvalue equation I meant [itex]A^{-1}(Ax)=A^{-1}\lambda x[/itex]. On a side note, use [tex]brackets around your entire equation not just lambda or mu.[/tex]
 
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So I can re-arrange as
[tex]\frac{A^{-1}(A\chi)}{\lambda} = \mu\chi[/tex]
Is that right?
 
Is A^-1*A the Inverse Matrix I?
 
Inverse multiplied by the eigenvector will give us the original matrix?
 
Are you calling I the inverse? It's the Identity matrix. If you let the Identity matrix work on a vector it yields that vector without making any changes to it. We're moving in the wrong direction now so let's start at the eigenvalue equation again. [itex]A^{-1}(Ax)=A^{-1}\lambda x \Rightarrow Ix=\lambda \mu x[/itex]. Now solve for [itex]\mu[/itex].
 
Oops, my bad, I meant Identity.

Ix gives us x again.

So:

[tex]\frac{I\chi}{\lambda\chi} = \mu[/tex]

Is that right? and Ix = x?
 
Well x are vectors how do you define a vector divided by another vector? While doing it in this case and letting the xs cancel you will get the correct answer, I would suggest you don't do this on your exam.

This is what you should do, [itex]Ix=\lambda \mu x \Rightarrow x=\lambda \mu x \Rightarrow 1*x=\lambda \mu x[/itex]. So what is [itex]\mu[/itex]?
 
[tex]\mu[/tex] = [tex]\frac{1}{\lambda}[/tex]

Correct?
 
Yes, perhaps it's nice if you write the full proof down now so we can see if you don't do any operations that you shouldn't really be doing.
 
Alright Cool, Here Goes!

[tex]A\chi = \lambda\chi[/tex]

[tex]A^{-1}\chi = \lambda^{-1}\chi[/tex]

Let [tex]\lambda^{-1} = \mu[/tex]

[itex] A^{-1}(Ax)=A^{-1}\lambda x[/itex]

We can re-write this as

[tex] \frac{A^{-1}(A\chi)}{\lambda} = \mu\chi[/tex]

[itex] A^{-1}A = I[/itex]

Here I is the Identity Matrix

Ix = x

As The Identity matrix multiplied by a vector value does not change the position of the vector. So we now have:

[tex] \frac{I\chi}{\lambda\chi} = \mu[/tex]

This equates to:

[tex]\frac{1}{\lambda\chi}[/tex]

This shows that the vector [tex]\chi[/tex] is an eigen vector for the Inverse Matrix of A as it's Eigen Value is also the Inverse of [tex]\chi[/tex]
 
Doesy said:
Alright Cool, Here Goes!

[tex]A\chi = \lambda\chi[/tex]

[tex]A^{-1}\chi = \lambda^{-1}\chi[/tex]

Let [tex]\lambda^{-1} = \mu[/tex]

You need to proof that [itex]\mu=\lambda^{-1}[/itex], not start with it.

What we are given is the eigenvalue equation [itex]Ax=\lambda x[/itex] with [itex]\lambda[/itex] being an eigenvalue of A. We want to proof that x is an eigenvector of [itex]A^{-1}[/itex] with eigenvalue [itex]\lambda^{-1}[/itex].
So let's multiply the eigenvalue equation by [itex]A^{-1}[/itex].
This yields
[tex]A^{-1}(Ax)=A^{-1} \lambda x \Rightarrow (A^{-1}A)x=\lambda A^{-1} x \Rightarrow Ix=\lambda A^{-1}x \Rightarrow 1*x=\lambda A^{-1} x \Rightarrow \lambda^{-1}x=A^{-1}x[/tex].
A matrix working on a vector that yields the same vector multiplied by a scalar is an eigenvalue equation. So x must be an eigenvector of [itex]A^{-1}[/itex] and [itex]\lambda^{-1}[/itex] must be an eigenvalue of [itex]A^{-1}[/itex]. There is no dividing of any vectors happening here! If you want to divide two vectors you must first define how division with vectors works.
 
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Ohhhh!

Thanks heaps man, is there anyway I can +rep you or something?