Prove Matrix Rank of A*A = Rank of A

In summary: So if Ker(A)\subseteq Ker(A^*A)Then by taking the dimension of each side and applying the rank-nullity theorem and applying my original result then we get the...In summary, the homework statement is that for all matricies A with real entries, we have rk(A*A)=rk(A).
  • #1
Maybe_Memorie
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0

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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  • #2
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...
 
  • #3
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}
 
  • #4
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.
 
  • #5
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).
 
  • #6
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:
 
  • #7
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*?
 
  • #8
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..
 
  • #9
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.
 

1. What is the definition of "matrix rank"?

The matrix rank of a matrix A is the maximum number of linearly independent rows or columns in A. In other words, it is the number of non-zero rows or columns in the matrix after it has been reduced to row-echelon form.

2. How do you prove that the matrix rank of A*A is equal to the rank of A?

To prove that the matrix rank of A*A is equal to the rank of A, we use the fact that multiplying a matrix by an invertible matrix does not change its rank. Therefore, we can rewrite A*A as (A*P)*(P^-1*A), where P is an invertible matrix. By the associative property of matrix multiplication, this becomes A*(P*P^-1)*A, which simplifies to A*A. Since P*P^-1 is the identity matrix, we can conclude that A*A has the same rank as A.

3. Can you provide an example to illustrate the proof of matrix rank of A*A = rank of A?

Yes, consider the following matrix A:

A = [1 2 3]

Its rank is 1, as there is only one linearly independent row. Now, let's multiply A by an invertible matrix P:

P = [1 0 0; 0 2 0; 0 0 1]

This results in A*P = [1 4 3], which also has a rank of 1. We can now rewrite this as (A*P)*(P^-1*A), where P^-1 = [1 0 0; 0 1/2 0; 0 0 1]. By the associative property, this becomes A*(P*P^-1)*A, or A*A. Since P*P^-1 is the identity matrix, we can conclude that A*A has the same rank as A (1).

4. What is the significance of proving the matrix rank of A*A = rank of A?

Proving the matrix rank of A*A = rank of A is significant because it helps us better understand the properties of matrix multiplication and the effects it has on the rank of a matrix. It also allows us to simplify calculations and reduce the amount of computation needed in certain matrix operations.

5. Are there any exceptions to the rule that the matrix rank of A*A = rank of A?

Yes, there are some exceptions to this rule. One exception is when A is a zero matrix, in which case the rank of A*A would be 0, while the rank of A is undefined. Another exception is when A is a non-square matrix, in which case the rank of A*A may not necessarily be equal to the rank of A.

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