Prove Matrix Rank of A*A = Rank of A

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Homework Help Overview

The discussion revolves around proving that for any nxn matrix A with real entries, the rank of the product of A and its transpose (A*A) is equal to the rank of A. Participants are exploring concepts related to linear transformations, image, and kernel of matrices.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants are examining the relationship between the image and kernel of the matrices A and A*A, questioning the validity of subset claims regarding these sets. They are also discussing the implications of the kernel and image definitions in the context of linear transformations.

Discussion Status

The discussion includes various attempts to establish the relationship between the kernels of A and A*A, with some participants providing insights and others expressing confusion. There is an ongoing exploration of how to prove the subset relationships and the implications of the rank-nullity theorem.

Contextual Notes

Participants are operating under the assumption that they are working with real-valued matrices and are required to prove the rank equality without providing a complete solution. There is a focus on understanding the definitions and properties of linear transformations.

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Homework Statement



Show that for all nxn matricies A with real entries we have
rk(A*A) = rk(A) where A* is the transpose of A.

Homework Equations





The Attempt at a Solution



I'm working over a vector space V.

Im(A) = {A(v) | vEV}

Im(A*A) = {A*(A(v)) | vEV}

So Im(A*A) is a subset of Im(A)
So rk(A*A) =< rk(A)

Ker(A) = {A(w) = 0 | wEV}

Ker(A*A) = {A*(A(w)) = 0 | wEV}

so Ker(A*A) is a subset of Ker(A)
so dimKer(A*A) =< dimKer(A)

dimKer(A) = dimV - rk(A)

so -rk(A*A) =< -rk(A)
so rk(A*A) >= rk(A)

Combining the results yields rk(A*A) = rk(A)


Is this correct??
 
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Maybe_Memorie said:
Im(A*A) = {A*(A(v)) | vEV}

So Im(A*A) is a subset of Im(A)

Ker(A*A) = {A*(A(w)) = 0 | wEV}

so Ker(A*A) is a subset of Ker(A)

Why are these two things true?? They don't exactly seem trivial to me...
 
Maybe_Memorie said:
Ker(A) = {A(w) = 0 | wEV}

Note that you have reversed your definition of Ker.
It should be:
Ker(A) = {wEV | A(w) = 0}
 
Im(A*A) = {A*(A(v)) | vEV}

A maps the vector v to another vector v1.
Then A* maps the vector v1 to another vector v2, but the set of all v1s can either be less than or equal to the set of original vectors, then A* has either the original amount of vectors or a smaller amount to map to v2.
So Im(A*A) is a subset of Im(A)

Similar logic can be applied to the Kernel.
 
Maybe_Memorie said:
Im(A*A) = {A*(A(v)) | vEV}

A maps the vector v to another vector v1.
Then A* maps the vector v1 to another vector v2, but the set of all v1s can either be less than or equal to the set of original vectors, then A* has either the original amount of vectors or a smaller amount to map to v2.

That only means that Im(A*A) has less elements than Im(A), which is true. This doesn't imply that it's a subset. You didn't prove that every element in Im(A*A) is in Im(A).
 
micromass said:
That only means that Im(A*A) has less elements than Im(A), which is true. This doesn't imply that it's a subset. You didn't prove that every element in Im(A*A) is in Im(A).

Ah I see. Any points in the right direction? :smile:
 
Maybe_Memorie said:
Ah I see. Any points in the right direction? :smile:

Yes. The trick is to prove that

[tex]Ker(A^*A)=Ker(A)[/tex]

Now, I claim that [itex]Ker(A)\subseteq Ker(A^*A)[/itex] (why??)
To see the other inclusion, assume that A*Ax=0. What happens if you multiply both sides with x*?
 
micromass said:
Yes. The trick is to prove that

[tex]Ker(A^*A)=Ker(A)[/tex]

Now, I claim that [itex]Ker(A)\subseteq Ker(A^*A)[/itex] (why??)
To see the other inclusion, assume that A*Ax=0. What happens if you multiply both sides with x*?

A*Ax = 0, then A*Axx* = 0
But if x is a nx1 matrix and x* a 1xn matrix xx* is a 1x1 matrix?
But the only way this can be zero is if either A*A is 0?

As for the kernel fact, I'm a bit confused..
 
Maybe_Memorie said:
A*Ax = 0, then A*Axx* = 0
But if x is a nx1 matrix and x* a 1xn matrix xx* is a 1x1 matrix?
But the only way this can be zero is if either A*A is 0?

Multiply the other side by x* :smile:

And use that in general z*z=|z| for vectors z.

As for the kernel fact, I'm a bit confused..

Let x be in Ker(A). You know that A(x)=0. Can you prove that A*A(x)=0?
 
  • #10
micromass said:
Multiply the other side by x* :smile:

And use that in general z*z=|z| for vectors z.



Let x be in Ker(A). You know that A(x)=0. Can you prove that A*A(x)=0?

Well if Ax = 0 then A*A(x) = A*(0) = 0, as under a linear operator 0 is always mapped to 0.
 
  • #11
Maybe_Memorie said:
Well if Ax = 0 then A*A(x) = A*(0) = 0, as under a linear operator 0 is always mapped to 0.

Yes, that's ok!
 
  • #12
I'm afraid I don't see how that shows

[itex]Ker(A)\subseteq Ker(A^*A)[/itex]
 
  • #13
Maybe_Memorie said:
I'm afraid I don't see how that shows

[itex]Ker(A)\subseteq Ker(A^*A)[/itex]

In words it says: if x is in the kernel of A, then it is also in the kernel of A*A.

Rephrasing: for every x in V we have: if Ax = 0, then A*Ax = 0.
 
  • #14
Right I understand now! :smile:

So if [itex]Ker(A)\subseteq Ker(A^*A)[/itex]

Then by taking the dimension of each side and applying the rank-nullity theorem and applying my original result then we get the answer?
 
  • #15
Nope.
Previously you had the subset relationship the wrong way around.
You'll see if you try it again.
Sorry.
 

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