Proving a matrix is diagonaziable.

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In summary, we are given a matrix H from M_n(C) and we know that for every a in C, dim(ker(H-aI)^2)<=1. We want to prove that H is diagonizable. We know that the characteristic polynomial alone cannot determine this, but the minimal polynomial may provide useful information. Since the minimal polynomial is a product of linear polynomials over C, we do not have higher degrees to consider. However, we still need information on the minimal polynomial to determine the maximal size of any Jordan blocks. To prove diagonalizability, we can show that for any Jordan block of size 2 or more, there exist vectors x and y such that Mx=tx and My=ty+x. If we
  • #1
MathematicalPhysicist
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im given a matrix H from M_n(C) (the space of nxn matrices above the comples field).
and we know that for every a in C, dim(ker(H-aI)^2)<=1.
prove that H is diagonizable.

obviously if i prove that its characteristic poly exists then bacuase every poly above C can be dissected to linear factors, then also its minimal poly can be dissected to linear factors and thus H is diagonaizable, but how to do it?

thanks in advance.
 
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  • #2
Every matrix has a characteristic poly, so your argument is invalid. The characteristic poly doesn't tell you anything about diagonalizability. The minimal poly may tell you something useful.
 
  • #3
care to elaborate a bit more?
i mean what exactly the minimial polynomial can help here?
 
  • #4
If the minimal polynomial has no repeated roots, then...
 
  • #5
do you know what a jordan form is?
 
  • #6
morphism said:
If the minimal polynomial has no repeated roots, then...

you mean that it is decomposed of linear polynomials, and thus the matrix is diagonizable.
but how to translate it to here.
i mean I am given that d=dimKer(H-xI)^2<=1, now if H-xI=0 then we have that minimal poly is linear product, but i don't see how to arrive at this, if i prove that dimKer(H-xI)=n then I am done, but how to do it?

p.s
mathwonk, i do know what is jordan noraml form is, but i don't see here relevancy?
 
  • #7
Why don't you see the relevance of JNF? It gives the answer instantly. A matrix is not diagonalizable if and only if there is a jordan block of size greater than 1. But the condition you have precludes that happening.
 
  • #8
but the maximum size of jordan block is determined by the multicipity of the eigen value in the minimal polynomial.
oh, wait a minute, bacuase it's above C, then it means that the minimal poly is product of linear polys, so obviously we don't have higher degrees, and
Ker(H-aI) is a subspace of Ker(H-aI)^2, so dim(Ker(H-aI))<=1, so we have just one block at most, but every Ker(H-aI)^2 its dim is <= 1, so obviously we don't have here dome subspace which nullifies the entire C^n, but still we ned to have information on the minimal poly to gain information about the maximal size of the block.
 
  • #9
loop quantum gravity said:
but the maximum size of jordan block is determined by the multicipity of the eigen value in the minimal polynomial.


yes.

oh, wait a minute, bacuase it's above C, then it means that the minimal poly is product of linear polys, so obviously we don't have higher degrees,

No. There is nothing that states that the linear factors of the minimal poly of a matrix are distinct, irrespective of the field.


and Ker(H-aI) is a subspace of Ker(H-aI)^2, so dim(Ker(H-aI))<=1, so we have just one block at most,

Absolutely not. If there is one block, then it can't be diagonalizable.

but every Ker(H-aI)^2 its dim is <= 1, so obviously we don't have here dome subspace which nullifies the entire C^n, but still we ned to have information on the minimal poly to gain information about the maximal size of the block.

I have no idea what you mean.

If we have a Jordan block of size 2, or more, and e-value t, then there are vectors x and y such that Mx=tx, and My=ty+x. That is the definition of a Jordan block. Now show that x and y are killed by (M-t)^2.
 

Related to Proving a matrix is diagonaziable.

1. What does it mean for a matrix to be diagonalizable?

A matrix is diagonalizable if it can be transformed into a diagonal matrix through a similarity transformation. This means that the matrix can be expressed as a product of three matrices: P, D, and P-1, where P is an invertible matrix and D is a diagonal matrix.

2. How can I determine if a matrix is diagonalizable?

A matrix is diagonalizable if it has n linearly independent eigenvectors, where n is the size of the matrix. This can be checked by finding the eigenvalues of the matrix and then finding the corresponding eigenvectors. If there are n distinct eigenvalues, then the matrix is diagonalizable.

3. Can a non-square matrix be diagonalizable?

No, a non-square matrix cannot be diagonalizable. Only square matrices can have eigenvalues and eigenvectors, which are necessary for a matrix to be diagonalizable.

4. What are the benefits of having a diagonalizable matrix?

Having a diagonalizable matrix makes it easier to perform calculations such as matrix multiplication and finding powers of the matrix. It also simplifies the process of finding the inverse of the matrix.

5. How can I prove that a matrix is diagonalizable?

To prove that a matrix is diagonalizable, you can follow these steps: 1. Find the eigenvalues of the matrix.2. For each eigenvalue, find the corresponding eigenvectors.3. If there are n distinct eigenvalues and n linearly independent eigenvectors, then the matrix is diagonalizable.4. Construct the diagonal matrix D using the eigenvalues as the diagonal elements.5. Construct the invertible matrix P using the eigenvectors as its columns.6. Multiply P, D, and P-1 to show that the original matrix can be expressed as a product of these matrices, thus proving that it is diagonalizable.

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