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

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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 [tex]\textrm{rank}(A) = \textrm{rank}(A^T A)[/tex] 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!