What Can Be Said About the Rows and Columns of \(A^TA\)?

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  • Thread starter Thread starter Karnage1993
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    Independence Linearly
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Discussion Overview

The discussion centers on the properties of the matrix product \(A^TA\) where \(A\) has linearly independent columns. Participants explore the implications for the rows and columns of \(A^TA\), particularly in relation to the rank and nullity of \(A\) and \(A^TA\). The scope includes theoretical considerations and mathematical reasoning.

Discussion Character

  • Technical explanation, Debate/contested, Mathematical reasoning

Main Points Raised

  • One participant states that if \(A\) has linearly independent columns, then \(A^T\) has linearly independent rows, prompting a question about the implications for \(A^TA\).
  • Another participant claims that \(\textrm{rank}(A) = \textrm{rank}(A^TA)\), suggesting that the number of linearly independent columns/rows of \(A\) is the same as that of \(A^TA\).
  • A different participant questions the validity of the rank equality, suggesting it may only hold if \(A\) is symmetric and notes that \(A\) is not necessarily square.
  • In response, a participant argues that the rank equality holds in general and provides reasoning based on the nullspace of \(A\) and \(A^TA\).
  • Another participant acknowledges a previous misunderstanding about the rank equality, indicating that they have found confirmation of its general validity.

Areas of Agreement / Disagreement

There is disagreement regarding the conditions under which \(\textrm{rank}(A) = \textrm{rank}(A^TA)\) holds, with some participants asserting it is true in general while others initially believed it was conditional on \(A\) being symmetric. The discussion remains unresolved regarding the implications of \(A\) not being square.

Contextual Notes

Participants express uncertainty about the implications of the matrix dimensions and the conditions under which the rank equality holds, particularly in relation to the symmetry of \(A\) and its square status.

Karnage1993
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Say I have a matrix ##A## that has linearly independent columns. Then clearly ##A^T## has lin. indep. rows. So what can we say about ##A^TA##? Specifically, is there anything we can say about the rows/columns of ##A^TA##? I'm thinking there has to be some sort of relation but I don't know what that is (if there is indeed any).
 
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We have \textrm{rank}(A) = \textrm{rank}(A^T A) So the number of linear independent columns/rows of ##A## is the same as the number of linear independent columns/rows of ##A^T A##.
 
Isn't ##\textrm{rank}(A) = \textrm{rank}(A^TA)## only true if ##A## is symmetric? Also, I forgot to include that ##A## is not necessarily a square matrix. Let's have ##A## be an ##n## x ##k## matrix. Does your conclusion still follow with these new conditions?
 
Yes, it is true in general. Indeed, by rank-nullity is suffices to show that the nullity of ##A## equals the nullity of ##A^T A##.

But take ##Ax = 0##, then obviously ##A^T A x = 0##.
Conversely, if ##A^T A x = 0##, then ##x^T A^T A x##. But then ##|Ax| = 0##. Thus ##Ax= 0##.

So the nullspace of ##A## equals the nullspace of ##A^T A##.
 
I was under the impression that ##\textrm{rank}(A) = \textrm{rank}(A^TA)## is only true if ##A## is symmetric, but it appears you are right, and Wikipedia confirms it. It is indeed true in general for any ##A##, so I guess I misread it somewhere. Thanks for the help!
 

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