Linear algebra identities of inverse matricies

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Homework Help Overview

The discussion revolves around the properties of left and right inverses of matrices, specifically in the context of rectangular matrices. The original poster attempts to demonstrate the existence of left and right inverses for a given matrix A and to verify the corresponding identities.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • Participants explore the definitions and properties of left and right inverses, with attempts to manipulate matrix equations to show the identities hold. Questions arise regarding the assumptions necessary for the existence of inverses and the validity of certain steps in the proofs.

Discussion Status

Some participants provide guidance on the correct notation and approach to take when dealing with matrix inverses. There is a mix of interpretations regarding the proofs for left and right inverses, with some expressing uncertainty about the similarities between the two cases.

Contextual Notes

There is a noted concern about the assumptions regarding the existence of inverses for the matrices involved, as well as the clarity of notation used in the attempts to prove the identities.

SpiffyEh
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Homework Statement


Left Inversion in Rectangular Cases. Let A[tex]^{-1}_{left}[/tex] = (A[tex]^{T}[/tex]A)[tex]^{-1}[/tex]A[tex]^{T}[/tex] show A[tex]^{-1}_{left}[/tex]A = I.

This matrix is called the left-inverse of A and it can be shown that if A [tex]\in[/tex] R[tex]^{m x n}[/tex] such that A has a pivot in every column then the left inverse exists.

Right Inversion in Rectangular Cases. Let A[tex]^{-1}_{right}[/tex] = A[tex]^{T}[/tex](AA[tex]^{T}[/tex])[tex]^{-1}[/tex]. Show AA[tex]^{-1}_{right}[/tex] = I.

This matrix is called the right-inverse of A and it can be shown that if A [tex]\in[/tex] R[tex]^{m x n}[/tex] such that A has a pivot in every row then the right inverse exists.

Homework Equations





The Attempt at a Solution


I tried the left part and this is what I did:
A[tex]^{-1}_{left}[/tex] / (A[tex]^{T}[/tex]A)[tex]^{-1}[/tex]= A[tex]^{T}[/tex]
A[tex]^{-1}_{left}[/tex](A[tex]^{T}[/tex]A) = A[tex]^{T}[/tex]
A[tex]^{-1}_{left}[/tex]A = A[tex]^{T}[/tex]( A[tex]^{T}[/tex])[tex]^{-1}[/tex] = I

I'm not sure if this is correct or not, so I want to see if I have the right idea. I know that A*A[tex]^{-1}[/tex] = I so I thought this would work. Also isn't the right one the exact same thing? Or do I have to do that one a different way? Oh and can someone also explain the concept of left and right inverse. I don't really understand it. Thanks
 
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Sorry it's not showing right at all. I'll try to make it clearer. Al is A.

Attempt:
Al^(-1) / (A^T * A)^(-1) = A^T
Al^(-1) * (A^T * A) = A^T
Al^(-1) * A = A^T * (A^T)^(-1) = I
therefore, Al^(-1) * A = I

Hopefully that made what I was trying to do more clear

Can someone please help?
 
Don't write divide when dealing with matrices, use the inverse notation.
Write the following to compute the inverse of [tex]A^{T}A[/tex]
[tex] (A^{T}A)^{-1}(A^{T}A)=I[/tex]
Multiply on the right by the appropriate stuff to find the expression for the inverse and then use this in the definition of the left inverse. I should come out in the wash.
 
So, I did the (A[tex]^{T}[/tex]A)[tex]^{-1}[/tex](A[tex]^{T}[/tex]A) = I
and because of the property AA[tex]^{-1}[/tex] = I the left side is I. So, is this proff enough for Aleft? Is Aright basically the same thing then?
 
I was trying to get you to show that
[tex] (A^{T}A)^{-1}=A^{-1}(A^{T})^{-1}[/tex]
Then use this in the definition of the left inverse to compute that
[tex] A_{left}^{-1}A=I[/tex]
 
Are you allowed to assume that [itex]A^{-1}[/itex] and [itex](A^T)^{-1}[/itex] exist?
 
Hmm, most likely not! my bad...
 
And Spiffy, I think you're done in your proof.

Mat
 
oh ok, thank you. Would I do the same thing for Aright? I'm paranoid because it seems the same but I'm asked about it as well so I expect it to be different.
 
  • #10
I think you'll be fine. Nothing to worry about.

Mat
 

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