Show that for a symmetric or normal matrix

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Discussion Overview

The discussion revolves around demonstrating that for a symmetric or normal matrix A, the determinant of A equals the product of its eigenvalues, det(A) = ∏ λ_i. Participants explore various approaches to prove this result, including unitary equivalence and the Cayley-Hamilton theorem, while avoiding the use of Jordan blocks.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant suggests using unitary equivalence and the properties of symmetric matrices to show that det(A) = det(D), where D is a diagonal matrix, but questions how to confirm that the diagonal entries are the eigenvalues.
  • Another participant mentions the Cayley-Hamilton theorem as a potential method, arguing that the result holds for any square matrix, not just symmetric or normal ones.
  • A different participant proposes considering the spectral theorem for normal matrices as a shortcut to the proof.
  • One participant asserts that every symmetric or normal matrix can be diagonalized, leading to the conclusion that the determinant of A equals the product of its eigenvalues through the relationship between the determinants of similar matrices.

Areas of Agreement / Disagreement

Participants express differing opinions on the necessity of using the Cayley-Hamilton theorem and the best approach to demonstrate the relationship between the determinant and eigenvalues. No consensus is reached on a single method to prove the statement.

Contextual Notes

Some participants' arguments depend on the definitions of symmetric and normal matrices, and the discussion does not resolve the specific mathematical steps required to confirm that the diagonal entries correspond to the eigenvalues.

MatthewD
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Is there anyway to show that for a symmetric or normal matrix A, that det(A) = [tex]\prod \lambda_i[/tex] without using Jordan blocks? I want to show this result using maybe unitary equivalence and other similar matrices... any ideas? It's obviously easy with JCF...
 
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I don't know what is the function of JCF in it...it simply follows from the well known Caley-Hamilton Theorem (Every square matrix satisfies its own characteristic equation) and the result holds for any square matrix.
 


Do I have to use Cayley-Hamilton? Could I use the fact that A would be orthogonally equivalent to a diagonal matrix by definition of symmetric, so for some orthogonal matrix Q and diagonal matrix D:
A=Q*DQ
then det(A)=det(Q*DQ)=det(D)
D is diagonal=>det(D)=product of diagonal entries... but how would I show these are the eigenvalues?
if they're the eigenvalues, then i have my result since similar matrices have the same eigenvalues...
 


Every symmetric, or normal, matrix, A, can be diagonalized- that is, there exist an invertible matrix P such that [itex]PAP^{-1}= D[/itex] where D is a diagonal matrix having the eigenvalues of A on its diagonal.

Now [itex]det(PAP^{-1})=[/itex][itex]det(P)det(A)det(P)^{-1}= det(A)= det(D)[/itex] and that last is, of course, the product of the eigenvalues.
 

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