Effect of orthonormal projection on rank

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Homework Help Overview

The discussion revolves around the relationship between the rank of a matrix and its QR factorization, specifically examining how the multiplication by a full rank orthonormal matrix affects the rank.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants explore the implications of multiplying a full rank orthonormal matrix Q with an upper trapezoidal matrix R, questioning whether rank(Q*R) equals rank(R) and rank(A).

Discussion Status

Some participants suggest that the rank remains unchanged under the transformation, while others question the conditions under which this holds true, particularly focusing on the properties of the orthonormal matrix Q.

Contextual Notes

There is an underlying assumption that Q is full rank and orthonormal, which is central to the discussion of rank preservation.

kalleC
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Homework Statement


Given rank(R) and a QR factorization A = QR, what is the rank(A)


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The Attempt at a Solution


I want to know if multiplication by a full rank orthonormal matrix Q and an upper trapezoidal matrix R yields rank(R)=rank(Q*R)=rank(A)

This is mostly guesswork by me but I'd like to use it for a question I need to answer.
 
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Well, the rank of a matrix is the dimension of the image, right? If the image of R is a subspace S of dimension rank(R), then what's the dimension of Q(S) if Q is full rank?
 
They are equal? As the only way Q(S) would be dissimilar would be if rank(Q)<rank(R).

But does not the reason for this have anything to do with Q being orthnormal? Otherwise couldn't Q act on R and cause some of the image to overlap effectively reducing the rank?
 
Q is full rank, so it's one to one. So yes, rank(QR)=dim(Q(S))=dim(S)=rank(R). So rank(QR)=rank(R).
 
Thank you very much =)
 

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