[Linear Algebra] rank(AT A) = rank(A AT)

In summary: Sorry, it's kind of late here and for some reason this question is making me see double. I'll give you one thing you haven't used yet and that's if V is a subspace of a vector space of dimension n then dim(V^\perp)+dim(V)=n. Hope that helps. I'll have another look in the morning, and if you've gotten it I'll be really happy. I think you're close.In summary, the question asks if the equation \text{rank}(A^T A = \text{rank}(A A^T) holds for all nxm matrices A, with the hint to use the previous exercise which states \text{ker}(A) = \text{ker
  • #1
macaholic
22
0

Homework Statement


Does the equation [itex]\text{rank}(A^T A = \text{rank}(A A^T)[/itex] hold for all nxm matrices A? Hint: the previous exercise is useful.

Homework Equations


[itex] \text{ker}(A) = \text{ker}(A^T A) [/itex]
[itex] \text{dim}(\text{ker}(A) + \text{rank}(A) = m [/itex]

The Attempt at a Solution


The previous exercise it referring to asked to show that [itex]\text{rank}(A) = \text{rank}(A^T A) [/itex] holds for all nxm matrices A.
Which I did by stating:
[itex] \text{ker}(A) = \text{ker}(A^T A) [/itex]
and then taking the dimension of both sides, using the rank-nullity theorem to get:
[itex] n-\text{rank}(A) = n - \text{rank}(A^T A)[/itex] which makes it clearly true.

I tried using this result to prove the stated problem like so:
[itex]\text{rank}(A) = \text{rank}(A^T A) [/itex]
[itex]\text{rank}(A^T A) = \text{rank}((A^T A)^T A^T A) [/itex]
[itex]= \text{rank}(A^T A A^T A) [/itex]
But then I get promptly stuck because I'm not sure what to do with the right side of that. Any advice?
 
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  • #2
macaholic said:

Homework Statement


Does the equation [itex]\text{rank}(A^T A = \text{rank}(A A^T)[/itex] hold for all nxm matrices A? Hint: the previous exercise is useful.

Homework Equations


[itex] \text{ker}(A) = \text{ker}(A^T A) [/itex]
[itex] \text{dim}(\text{ker}(A) + \text{rank}(A) = m [/itex]

The Attempt at a Solution


The previous exercise it referring to asked to show that [itex]\text{rank}(A) = \text{rank}(A^T A) [/itex] holds for all nxm matrices A.
Which I did by stating:
[itex] \text{ker}(A) = \text{ker}(A^T A) [/itex]
and then taking the dimension of both sides, using the rank-nullity theorem to get:
[itex] n-\text{rank}(A) = n - \text{rank}(A^T A)[/itex] which makes it clearly true.

I tried using this result to prove the stated problem like so:
[itex]\text{rank}(A) = \text{rank}(A^T A) [/itex]
[itex]\text{rank}(A^T A) = \text{rank}((A^T A)^T A^T A) [/itex]
[itex]= \text{rank}(A^T A A^T A) [/itex]
But then I get promptly stuck because I'm not sure what to do with the right side of that. Any advice?

Didn't you also prove rank(A)=rank(A^T)? I.e. column rank equal row rank?
 
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  • #3
Dick said:
Didn't you also prove rank(A)=rank(A^T)?
Funny you should mention that... That was the problem BEFORE the previous problem, which I still haven't figured out quite yet.

In any case, I suppose I can use that fact. But I'm not quite sure how it applies since [itex](A^T A)^T = A^T A[/itex]
So I guess I have these equalities:
[itex]\text{rank}(A)=\text{rank}(A^T) = \text{rank}(A^T A) [/itex]
I'm not sure how to rearrange this to get [itex]\text{rank}(A^T A)[/itex]
 
  • #4
macaholic said:
Funny you should mention that... That was the problem BEFORE the previous problem, which I still haven't figured out quite yet.

In any case, I suppose I can use that fact. But I'm not quite sure how it applies since [itex](A^T A)^T = A^T A[/itex]
So I guess I have these equalities:
[itex]\text{rank}(A)=\text{rank}(A^T) = \text{rank}(A^T A) [/itex]
I'm not sure how to rearrange this to get [itex]\text{rank}(A^T A)[/itex]

But you also have rank(AA^T)=rank(A^T), don't you?
 
  • #5
Dick said:
But you also have rank(AA^T)=rank(A^T), don't you?
I do? I guess that what I'm missing, I can't currently see how that arises from the equalities I'm given.

It would make it trivial from there though, since then I would just say that [itex] \text{rank}(A A^T) = \text{rank}(A) = \text{rank}(A^T A)[/itex]

I keep wanting to "sub in" [itex]A A^T[/itex] to one of the equalities as a replacement for A, I assume this is the wrong approach?
 
  • #6
macaholic said:
I do? I guess that what I'm missing, I can't currently see how that arises from the equalities I'm given.

It would make it trivial from there though, since then I would just say that [itex] \text{rank}(A A^T) = \text{rank}(A) = \text{rank}(A^T A)[/itex]

I keep wanting to "sub in" [itex]A A^T[/itex] to one of the equalities as a replacement for A, I assume this is the wrong approach?

Your theorem tells you rank(B^TB)=rank(B) for ANY matrix B. Put B=A^T.
 
  • #7
Dick said:
Your theorem tells you rank(B^TB)=rank(B) for ANY matrix B. Put B=A^T.
'doh, thanks a bunch! I should have been able to try that on my own.

While I'm here, would you mind helping with the other proof I'm stuck on?

It wanted me to use: [itex] (\text{im} A)^\perp = \text{ker}(A^T) [/itex] to prove [itex]\text{rank}(A)=\text{rank}(A^T)[/itex]

im is just the column space, and ker is the null space in my textbook.

I've gotten this far using rank-nullity but I got stonewalled:

[itex]\text{im } A = \text{ker}(A^T)^\perp [/itex]
[itex]\text{rank } A = \text{dim}(\text{ker}(A^T)^\perp ) = m - \text{dim } (\text{ker }A)[/itex]

I feel like this is probably the wrong way to approach this problem but I can't think of another.
 
  • #8
macaholic said:
'doh, thanks a bunch! I should have been able to try that on my own.

While I'm here, would you mind helping with the other proof I'm stuck on?

It wanted me to use: [itex] (\text{im} A)^\perp = \text{ker}(A^T) [/itex] to prove [itex]\text{rank}(A)=\text{rank}(A^T)[/itex]

im is just the column space, and ker is the null space in my textbook.

I've gotten this far using rank-nullity but I got stonewalled:

[itex]\text{im } A = \text{ker}(A^T)^\perp [/itex]
[itex]\text{rank } A = \text{dim}(\text{ker}(A^T)^\perp ) = m - \text{dim } (\text{ker }A)[/itex]

I feel like this is probably the wrong way to approach this problem but I can't think of another.

Sorry, it's kind of late here and for some reason this question is making me see double. I'll give you one thing you haven't used yet and that's if V is a subspace of a vector space of dimension n then [itex]dim(V^\perp)+dim(V)=n[/itex]. Hope that helps. I'll have another look in the morning, and if you've gotten it I'll be really happy. I think you can.
 
  • #9
Yeah, it's easy enough. [itex]m-dim(ker(A))=dim(ker(A)^\perp)[/itex]. Got it yet?

I just edited the above. I had a parenthesis is a bad place.
 
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FAQ: [Linear Algebra] rank(AT A) = rank(A AT)

1. What does the equation "rank(AT A) = rank(A AT)" mean in linear algebra?

The equation "rank(AT A) = rank(A AT)" means that the rank of the product of a matrix A and its transpose is equal to the rank of the transpose of A multiplied by A. In other words, the number of linearly independent rows or columns in the matrix A is the same as the number of linearly independent rows or columns in the product of A and its transpose.

2. Why is the equation "rank(AT A) = rank(A AT)" important in linear algebra?

The equation "rank(AT A) = rank(A AT)" is important because it provides a way to calculate the rank of a matrix without having to compute its determinant or eigenvalues. This can save time and computational resources, especially for larger matrices. Additionally, it is a useful tool for solving systems of linear equations and understanding the structure of a matrix.

3. How can the equation "rank(AT A) = rank(A AT)" be applied in real-world problems?

The equation "rank(AT A) = rank(A AT)" has many applications in real-world problems. For example, it can be used in regression analysis to determine the best fit for a set of data points. It can also be used in image and signal processing to reduce the dimensionality of data and identify important features. Additionally, it is used in machine learning algorithms for tasks such as dimensionality reduction and feature selection.

4. Is the equation "rank(AT A) = rank(A AT)" always true?

Yes, the equation "rank(AT A) = rank(A AT)" is always true. This is a fundamental property of matrices and can be proven using basic linear algebra operations. However, it is important to note that the equation only holds for square matrices. For non-square matrices, the ranks may not be equal.

5. How can I use the equation "rank(AT A) = rank(A AT)" to determine if a matrix is invertible?

The equation "rank(AT A) = rank(A AT)" can be used to determine if a matrix is invertible by checking if its rank is equal to its square dimension. If the rank is equal, then the matrix is invertible. This is because an invertible matrix has full rank, meaning all of its rows and columns are linearly independent. On the other hand, if the rank is less than the square dimension, then the matrix is not invertible.

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