Polynomial of Jordan Decomposition

In summary: A) = Am, but not for p(A) = Am + Al …so we just prove it for Am, show that it's the same X for any m, and then use the theorem above to prove that it's true for any sum of powers of A, ie for any polynomial....
  • #1
azdang
84
0

Homework Statement


Let A be a real or complex nxn matrix with Jordan decomposition A = [tex]X \Lambda X^{-1}[/tex] where [tex]\Lambda[/tex] is a diagonal matrix with diagonal elements [tex]\lambda_1,...,[/tex] [tex]\lambda_n[/tex]. Show that for any polynomial p(x):
p(A)=[tex]Xp(\Lambda)X^{-1}[/tex]

[tex]p(\Lambda)[/tex] should really be the matrix with p([tex]\lambda_j[/tex]) on its diagonal for j=1,...,n but I couldn't figure out how to make that matrix in latex.

The Attempt at a Solution


I'm guessing there should be a way to take p of both sides and somehow extract the X and X inverse, but I can't seem to figure it out. Does anyone see anything? Thank you.
 
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  • #2
Hi azdang! :wink:

(for matrices etc in LaTeX, see http://www.physics.udel.edu/~dubois/lshort2e/node56.html#SECTION00850000000000000000 )

Hint: if the polynomial is A2, then A2= XΛX-1XΛX-1 = XΛ2X-1 = … ? :smile:
 
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  • #3
Hey tiny-tim. Thank you for your response. Quick question: Is it okay to assume p(A) = [tex]A^2[/tex]? (This would be the polynomial p(x)=[tex]x^2[/tex] right?) Can we say that because the problem say 'for any polynomial'? Or is there a more general way to prove this?

Also, I do see then that [tex]\Lambda^2=p(\Lambda)[/tex], so that is all clear. Thank you very much. I'm just wondering if we should find a way to represent p(x) more generally?
 
  • #4
azdang said:
Is it okay to assume p(A) = [tex]A^2[/tex]?

I'm just wondering if we should find a way to represent p(x) more generally?

Yes, we need to deal with a general p(x).

So you do still need to prove it for p(A) = An, for any n …

my n = 2 was just an example for you. :wink:
 
  • #5
Alright, cool.

So, we could say p(x) = xn.

Then, p(A)=An=[tex](X \Lambda X^{-1})^n[/tex]=[tex]X \Lambda X^{-1}X \Lambda X^{-1}...X \Lambda X^{-1}[/tex].

All of the X's and X-1 will cancel out except for the ones on the edges so we are left with: p(A)=[tex]X \Lambda^n X^{-1}[/tex].

So, we know p([tex]\Lambda[/tex])=[tex]\Lambda^n[/tex]. Can we say that that is equivalent to the matrix with p([tex]\lambda_j[/tex]) on its diagonal where j=1,...,n because it is a diagonal matrix?
 
  • #6
just got up :zzz: …

azdang said:
… So, we know p([tex]\Lambda[/tex])=[tex]\Lambda^n[/tex]. Can we say that that is equivalent to the matrix with p([tex]\lambda_j[/tex]) on its diagonal where j=1,...,n because it is a diagonal matrix?

Yes, of course (for Λm). :wink:

(I don't think the examiner would even expect a reason for that :wink:)

And then prove that it's "additive", in the sense that if B = XΛBX-1 and C = xΛCX-1 then ΛB+C = … ? :smile:
 
  • #7
I'm kind of confused by this last part. I don't really understand the subscripts on Lambda or where B and C are coming from. Thank you again, tiny-tim, for helping me get through this :)
 
  • #8
azdang said:
I'm kind of confused by this last part. I don't really understand the subscripts on Lambda or where B and C are coming from. Thank you again, tiny-tim, for helping me get through this :)

I mean, if B and C are real or complex nxn matrices with Jordan decomposition B = XΛBX-1 and C = XΛCX-1 and B + C = XΛX-1 where Λ ΛB and ΛC are diagonal matrices, what is the equation relating Λ ΛB and ΛC ? :smile:
 
  • #9
But we cannot be guaranteed that the X's in the Jordan decompositions for B and C are the same, can we?
 
  • #10
azdang said:
But we cannot be guaranteed that the X's in the Jordan decompositions for B and C are the same, can we?

?? :confused:

Assume they are … then prove the proposition … and then use it to answer the original question.
 
  • #11
Well then, does [tex]X\Lambda_B X^{-1} + X\Lambda_C X^{-1}=X(\Lambda_B + \Lambda_C)x^{-1}[/tex]?

Then [tex]p(\Lambda_B + \Lambda_C)=p(\Lambda_B)+p(\Lambda_C)[/tex], so the matrix would be the diagonal matrix with [tex]\lambda_B_j + \lambda_C_j[/tex] on the diagonal, j=1,...n, right? I'm not sure if I'm understanding all this, or why we are showing it is additive. Sorry!
 
  • #12
azdang said:
Well then, does [tex]X\Lambda_B X^{-1} + X\Lambda_C X^{-1}=X(\Lambda_B + \Lambda_C)x^{-1}[/tex]?

That's right! :smile:
Then [tex]p(\Lambda_B + \Lambda_C)=p(\Lambda_B)+p(\Lambda_C)[/tex], so the matrix would be the diagonal matrix with [tex]\lambda_B_j + \lambda_C_j[/tex] on the diagonal, j=1,...n, right? I'm not sure if I'm understanding all this, or why we are showing it is additive.

Because it's easy to prove the question for p(A) = Am, but not for p(A) = Am + Al

so we just prove it for Am, show that it's the same X for any m, and then use the theorem above to prove that it's true for any sum of powers of A, ie for any polynomial. :wink:
 
  • #13
Okay, I think I'm understanding, though I might have a hard time explaining it. One thing in what you just said that confused me, what do you mean 'show that it's the same X for any m'. I really can't thank you enough for walking me through this btw.
 
  • #14
azdang said:
One thing in what you just said that confused me, what do you mean 'show that it's the same X for any m'.

I mean, for example, A2 = XΛ2X-1 and A3 = XΛ3X-1 (and it's the same X, which was worrying you earlier :wink:)
 
  • #15
Oooh, okay. Yes, I can see right away that they are the same X!

So, I should really just show this for:
p(x)=[tex]\alpha_0+\alpha_1x+\alpha_2x^2+...+\alpha_nx^n[/tex] for scalars alpha.

P.S. I totally just did this ^ and I TOTALLY get it. Again, thank you very very much!
 

Related to Polynomial of Jordan Decomposition

What is a Polynomial of Jordan Decomposition?

A Polynomial of Jordan Decomposition is a mathematical process used to break down a complex matrix into simpler components, making it easier to analyze and solve problems.

What is the purpose of Polynomial of Jordan Decomposition?

The purpose of Polynomial of Jordan Decomposition is to simplify complex matrices and make them easier to work with. It also helps in finding eigenvalues and eigenvectors of a matrix.

What is the difference between Polynomial of Jordan Decomposition and Diagonalization?

Polynomial of Jordan Decomposition and Diagonalization are two different methods used to simplify matrices. Diagonalization only works for diagonalizable matrices, while Polynomial of Jordan Decomposition can be applied to any matrix.

What are the applications of Polynomial of Jordan Decomposition?

Polynomial of Jordan Decomposition has many applications in various fields such as physics, engineering, and economics. It is used to solve differential equations, analyze systems of linear equations, and study complex systems.

Is Polynomial of Jordan Decomposition a reversible process?

No, Polynomial of Jordan Decomposition is not a reversible process. Once a matrix is decomposed, it cannot be reconstructed back to its original form. However, the decomposition can be used to solve problems and analyze the matrix in its simpler form.

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