How do inverse matrices affect eigenvalues?

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Homework Help Overview

The discussion revolves around the relationship between a matrix and its inverse, specifically focusing on how the existence of an inverse affects the eigenvalues of the matrix. The original poster is tasked with showing that if a matrix has eigenvalues λᵢ, then the eigenvalues of its inverse are 1/λᵢ, provided the inverse exists.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • The original poster attempts various methods to prove the relationship, including solving for eigenvalues, exploring eigenvectors, and considering unitary transformations. They express frustration over the complexity of their symbolic attempts and seek guidance on a key relation they feel is missing.

Discussion Status

Some participants suggest a more straightforward approach by applying the definition of eigenvalues and eigenvectors directly to the inverse matrix equation. There is a back-and-forth regarding the validity of certain steps in the reasoning, with some clarifications offered about the treatment of eigenstates. The discussion appears to be progressing towards a clearer understanding, but lacks a definitive consensus.

Contextual Notes

The original poster indicates a desire for a general proof in three or more dimensions, which may imply constraints related to the dimensionality of the matrices being considered. There is also an underlying assumption that the matrix in question is invertible.

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Homework Statement


Given a matrix with eigenvalues \lambda_{i}, show that if the inverse of the matrix exists, its eigenvalues are \frac{1}{\lambda}.


Homework Equations





The Attempt at a Solution

This shouldn't be so hard. I've come up with a few trivial examples, but I would like to get a general proof in 3 (or more) dimensions. I've tried solving for the eigenvalues, getting the eigenvectors and trying a unitary transformation. I've tried substituting the product of the matrix with its inverse in the characteristic equation and playing around with that before actually calculating the determionant. I've tried other symbolic brute force attempts, but they get very complicated very quickly. I've researched a bunch of determinant identities to no avail. I feel that there must be some key relation that I'm just missing. Any help would be appreciated.
 
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Just write down the definition of eigenvalue and eigenvector, and then apply the inverse matrix to that equation. Nothing complicated involving determinants and/or unitary transformations are necessary.
 
Is this the idea?
$\begin{array}{l}<br /> \Lambda \left| \psi \right\rangle = \lambda \left| \psi \right\rangle \\ <br /> \Lambda ^{ - 1} \left( {\Lambda \left| \psi \right\rangle } \right) = \Lambda ^{ - 1} \left( {\lambda \left| \psi \right\rangle } \right) \\ <br /> \left( {\Lambda ^{ - 1} \Lambda } \right)\left| \psi \right\rangle = \left( {\Lambda ^{ - 1} \lambda } \right)\left| \psi \right\rangle \\ <br /> I\left| \psi \right\rangle = \lambda \Lambda ^{ - 1} \left| \psi \right\rangle \\ <br /> \left| \psi \right\rangle = \lambda \Lambda ^{ - 1} \left| \psi \right\rangle \\ <br /> I = \lambda \Lambda ^{ - 1} \\ <br /> \end{array}$
 
Fine except for the last line; you can't drop the state because it's not a generic state; it's an eigenstate of Lambda. You can, however, multiply both sides of the next-to-last line by 1/lambda.
 
Except for the very last line, you're there!
 
Yes, I see. Thanks very much!
 

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