Eigenvalues for an Invertible Matrix

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Homework Help Overview

The discussion revolves around the properties of eigenvalues and eigenvectors, specifically focusing on an invertible matrix A and its inverse A^-1. The original poster seeks to demonstrate that if x is an eigenvector of A with a non-zero eigenvalue λ, then x is also an eigenvector of A^-1 with eigenvalue λ^-1.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • Participants explore the relationship between the eigenvalue equation for A and its inverse A^-1, questioning how to manipulate these equations to show the desired result. There are discussions about the implications of multiplying by the inverse and the identity matrix.

Discussion Status

Participants have provided various insights and suggestions on how to approach the proof, including multiplying the eigenvalue equation by A^-1 and discussing the properties of the identity matrix. There is an ongoing exploration of the correct steps to take without reaching a final consensus on the proof structure.

Contextual Notes

Some participants express uncertainty about the definitions and operations involving eigenvalues and eigenvectors, particularly regarding division of vectors and the implications of the identity matrix in the context of eigenvalue equations.

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?
 
Sure, because the problem says [itex]\lambda \neq 0[/itex].What is [itex]A^{-1}A[/itex]?
 
Is A^-1*A the Inverse Matrix I?
 
Yes it's the identity matrix, which has itself as an inverse. What is Ix?
 
Inverse multiplied by the eigenvector will give us the original matrix?
 
  • #10
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].
 
  • #11
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?
 
  • #12
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]?
 
  • #13
[tex]\mu[/tex] = [tex]\frac{1}{\lambda}[/tex]

Correct?
 
  • #14
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.
 
  • #15
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]
 
  • #16
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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  • #17
Ohhhh!

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

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