Is Every Diagonalizable Endomorphism Defined by a Simple Polynomial?

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SUMMARY

The discussion centers on the theorem regarding diagonalizability of endomorphisms in finite-dimensional vector spaces over the complex numbers. It establishes that an endomorphism \( f \) is diagonalizable if and only if there exists a polynomial \( P \in K[X] \) such that \( P(f) = 0 \) and \( P \) can be expressed as a product of linear factors with distinct roots. The participants explore the necessary and sufficient conditions for diagonalizability, particularly focusing on the implications of the polynomial \( P(f) \) and the structure of eigenspaces associated with eigenvalues of \( f \). The conversation highlights confusion around the relationship between eigenvalues, eigenspaces, and the dimensions of the vector space.

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Hello, I am studying reduction of endomorphisms and I came across a theorem that I can't understand completely. It states that:

Theorem: Let ##E## be a finite dimensional ##K## vector space, ##K## sub-field of ##\mathbb{C}##, and ##f## be an endomorphism of ##E##. Then, ##f## is diagonalizable if and only if there exists a polynomial ##P## of ##K[X]##, such that ##P(f) = 0##, and ##P## can be written as a product of polynomials of degree 1, and its roots have order of multiplicity 1.

I understand the proof I have for the sufficient condition, but the proof for the necessary condition is hard to follow for me so I tried an alternate way. I would like you to tell me if this is correct please:

##\Leftarrow ## ) Assume that there exists distinct ##\lambda_i##'s for ##i = 1...p##, and a polynomial ##P## in the form ##P = a \prod_{i=1}^p (X - \lambda_i) ## such that ##P(f) = 0##.
I want to show that ##E = \bigoplus_{\lambda \in \text{Sp}(f) } E_{f,\lambda}##, which is a necessary and sufficient condition of diagonalizability.
We have that for any ##x\in E-\{0\}##, ## P(f)(x) = 0##. So there exists ##i \in \{ 1...p \}## such that ##f(x) = \lambda_i x##, and ##x## belongs to the eigenspace ##E_{f,\lambda_i}##. Therefore ##x\in \bigoplus_{\lambda \in \text{Sp}(f) } E_{f,\lambda}##, and ##E \subset \bigoplus_{\lambda \in \text{Sp}(f) } E_{f,\lambda}##. The other inclusion is trivial. So ##f## diagonalizable.
 
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geoffrey159 said:
We have that for any ##x\in E-\{0\}##, ## P(f)(x) = 0##. So there exists ##i \in \{ 1...p \}## such that ##f(x) = \lambda_i x##, and ##x## belongs to the eigenspace ##E_{f,\lambda_i}##.
I may be missing the obvious, but I don't understand this step. How can it be that each element of E is an eigenvector?
 
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If ##P(f) = 0##, then ##P(f)(x) = 0 ## for all ##x\in E##.
  1. If ##x = 0## then ##x \in E \cap \bigoplus_{\lambda \in \text{Sp}(f) } E_{f,\lambda}##
  2. If ##x\neq 0##, then ## 0 = P(f)(x) = a\prod_{i = 1}^p (f(x) - \lambda_i x)##. So at least one term in the product is equal to 0. Therefore, there exists ##i## such that ##f(x) - \lambda_i x = 0##, which means that ##x## belongs to the eigenspace associated to ##f## and eigenvalue ##\lambda_i##.
 
I'm very confused, and probably wasting your time, but let us take a trivial example.
##E={\mathbb C}²##, and ##f## the endomorphism represented (with the canonical basis) by the matrix ##\begin{pmatrix}1 & 0 \\
0 & 2 \end{pmatrix}##.
So ##f(a,b)=(a,2b)##, and ##P(X)=(X-1)(X-2)##.
Take ##x=(1,1)##. ##f(x)=(1,2)##, so ##x## is not an eigenvector of f.
But ##P(f(x))=f(f(x))-3f(x)+2x=f(1,2)-3(1,2)+2(1,1)=(1,4)-(3,6)+(2,2)=(0,0)##.
 
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Oooops, sorry it's me who's wasting your time, I realize I am completely confused with the notations. I need to re-do this. Sorry
 
proofreading
 
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geoffrey159 said:
Developping the polynomial ##Q(f)## as a sum, and setting ##n = \text{Card(Sp}(f))##, we see that for any vector ##x\in E##, the family ## (x,f(x),f^2(x),...,f^n(x))## is linearly dependent in ##E##. Therefore ## \text{dim}(E) \le n ##.
I'm somewhat troubled by this step, but maybe I'm wrong about the notation.
If I understood it correctly, ##{Sp}(f)## is the set of all eigenvalues of ##f##.

If that is indeed the case, ##n = \text{Card(Sp}(f))## is the number of eigenvalues of ##f##. You claim that ## \text{dim}(E) \le n ##. But that can't be true in general, because an endomorphism can have less distinct eigenvalues than the dimension of the vector space, and still be diagonalizable.

I also don't immediately see why the linearly dependence of the family ## (x,f(x),f^2(x),...,f^n(x))## for all x implies that ## \text{dim}(E) \le n ##.
 
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Lol, this is exactly the reason why I deleted my post :-) I need to work more on this. Thank you for your help
 
geoffrey159 said:
Lol, this is exactly the reason why I deleted my post :-) I need to work more on this. Thank you for your help
Ok, no problem. :)
 
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  • #10
But I'm not giving up on this, I want to find the solution :-)
 
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  • #11
Think I have it now.

In a previous post, we said that ##P(f) = 0## implies that the eigenvalues of ##f## are among the zeros of ##P##.
Then it had to be true that ## P(f) = 0 \iff Q(f) = 0 ##, where ##Q = \prod_{\lambda \in \text{Sp}(f)} (X - \lambda) ##.

Now I want to show that ## E = \bigoplus_{\lambda\in\text{Sp}(f)} \text{Ker}(f-\lambda e) ##. The inclusion ##\supset## is trivial, and we now want to show ##\subset##. We need to prove that any ##x\in E## has a decomposition over the eigenspaces of ##f## in the form ##x = x_1 + ... + x_n ##, where ##x_i \in \text{Ker}(f-\lambda_i e) ##.

That would be done if we could find ##n## polynomials ##Q_k## such that the endomorphism ##Q_k(f)## sends any ##x\in E## in the ##k##-th eigenspace and :
## 0 = Q(f) = e - \sum_{k=1}^n Q_k(f) ##.

So the following constraints must be satisfied:
  1. ##Q_k(f) \in \text{Ker}(f-\lambda_k e) \iff f(Q_k(f)) = \lambda_k Q_k(f) \iff X(f) \circ Q_k(f) = (\lambda_k e)(f)\circ Q_k(f) \iff ((X-\lambda_k ) Q_k)(f) =0 ##
  2. Existence of a non-zero constant ##\beta## such that ## Q = \beta \ (1 - \sum_{k=1}^n Q_k)##, and the ##Q_k##'s have the same degree as ##Q##.
So if ##Q_k## has the form ##Q_k = \alpha (X-\mu) \prod_{i\neq k} (X-\lambda_i)##, where ##\alpha,\mu## are constants to be determined, then the first constraint is satisfied, and ##Q_k## has the same degree as ##Q##. For the second constraint, we must satisfy that ## 1 - \sum_{k=1}^n Q_k ## has the same roots as ##Q##. So it seems logic to determine ##\alpha,\mu## such that ##Q_k(\lambda_i) = \delta_{ik} \iff \alpha = \frac{1}{(\lambda_k-\mu) \prod_{i\neq k} (\lambda_k - \lambda_i)}## and ##\mu## is not an eigenvalue of ##f##.

Finally, there exists a constant ##\beta \neq 0## such that ## Q = \beta (1 - \sum_{k=1}^n \frac{X-\mu}{\lambda_k -\mu} \prod_{i\neq k} \frac{ (X-\lambda_i)}{ (\lambda_k - \lambda_i)})##, and for any ##x\in E##, ##P(f)(x) = 0 \iff Q(f)(x) = 0 \Rightarrow x \in \bigoplus_{\lambda\in \text{Sp}(f)} \text{Ker}(f-\lambda e) ##, which proves that ##f## is diagonolizable.

Is it correct now ?
 
  • #12
Looks correct to me. I'm a little lost in your point 1) concerning the polynomials ##Q_k##, but the formula for these polynomials fits the bill.
 
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  • #13
Finally! That made me sweat ;-) Thank you very much Samy for your patience
 

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