A square matrix A with ker(A^2)= ker (A^3)

In summary: Since A^2x is in ker(A^2), what can you say?In summary, we are trying to prove that for a square matrix A, if ker(A^2)= ker(A^3), then ker(A^3)= ker(A^4). This means that A^2x=0 if and only if A^3x=0, and we need to show that A^3x=0 if and only if A^4x=0. The first part is easy to prove by multiplying both sides by A, but the second part requires using the fact that A^2x is in ker(A^2)= ker(A^3).
  • #1
yeland404
23
0

Homework Statement


a square matrix A with ker(A^2)= ker (A^3), is ker(A^3)= ker (A^4),verify...


Homework Equations





The Attempt at a Solution

 
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  • #2
What did you try already??
 
  • #3
micromass said:
What did you try already??

it means that A^2*vector x= 0 and A*x=0 has same result,then I really confused how to do the next step
 
  • #4
yeland404 said:
it means that A^2*vector x= 0 and A*x=0 has same result,then I really confused how to do the next step

No, it means that

[tex]A^2x=0~\Leftrightarrow~A^3x=0[/tex]

You need to prove that

[tex]A^3x=0~\Leftrightarrow~A^4x=0[/tex]
 
  • #5
micromass said:
No, it means that

[tex]A^2x=0~\Leftrightarrow~A^3x=0[/tex]

You need to prove that

[tex]A^3x=0~\Leftrightarrow~A^4x=0[/tex]

emm..., to the difinition it says that ker(A) is T(x)=A(x)=0
 
  • #6
yeland404 said:
emm..., to the difinition it says that ker(A) is T(x)=A(x)=0

Yes, but you're not working with ker(A) here, but with ker(A2).
 
  • #7
micromass said:
Yes, but you're not working with ker(A) here, but with ker(A2).

should define a matrix A and write A^2 as dot product of A & A,and also to A^3...it seems to be so complex, or use the block to divide the matrix into some small matrix?
 
  • #8
yeland404 said:
should define a matrix A and write A^2 as dot product of A & A,and also to A^3...it seems to be so complex, or use the block to divide the matrix into some small matrix?

Do you understand my Post 4??
 
  • #9
micromass said:
Do you understand my Post 4??

so times A on both side of the equation?
 
  • #10
yeland404 said:
so times A on both side of the equation?

No, you need to prove two things:

If [itex]A^3x=0[/itex], then [itex]A^4x=0[/itex]. This can indeed be accomplished by multiplying sides with A.

But you also need to prove that if [itex]A^4x=0[/itex], then [itex]A^3x=0[/itex].
 
  • #11
so Ker (A^2)=0 can lead to Ker(A^4)=0,then?
 
  • #12
NO ONE has said that Ker(A^2)= 0 so I do not understand why you are asking this question. You seem, frankly, to have no idea what the question is saying. ker(A^2)= ker(A^3) means, as micromass said, "A^2x= 0 if and only if A^3x= 0". You want to use that to prove "A^3x= 0 if and only if A^4x= 0". As micromass said, the first part is easy: if A^3x= 0 then, applying A to both sides, A^4x= A0= 0 which proves that ker(A^3) is a subset of ker(A^4). You still need to prove the other way: if A^4x= 0, then A^3= 0. You cannot just multiply by A^{-1} because you have no reason to think that A is invertible. But notice that ker(A^2)= ker(A^3) has not yet been used so it might help to note that A^4x= A^2(A^2x).
 

1. What is a square matrix?

A square matrix is a type of matrix where the number of rows is equal to the number of columns. For example, a matrix with 3 rows and 3 columns is a square matrix.

2. What is the kernel of a matrix?

The kernel of a matrix, also known as the null space, is the set of all vectors that when multiplied by the matrix result in a zero vector.

3. How is the kernel of A^2 related to the kernel of A^3?

If A is a square matrix, then the kernel of A^2 is a subset of the kernel of A^3. In other words, every vector in the kernel of A^2 is also in the kernel of A^3.

4. What does it mean for ker(A^2) to be equal to ker(A^3)?

When ker(A^2) is equal to ker(A^3), it means that there are no additional vectors in the kernel of A^3 that are not already in the kernel of A^2.

5. What are the implications of ker(A^2)= ker (A^3) in terms of the matrix A?

This means that the matrix A has a repeated kernel, meaning that the same vectors are being mapped to the zero vector multiple times. It also indicates that the matrix A is not invertible, as the null space is not just the zero vector.

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