Eigen Vectors, Geometric Multiplicities and more....

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Discussion Overview

The discussion revolves around the conditions for a matrix to be diagonalizable, specifically focusing on the concept of geometric multiplicities of eigenvalues and their relationship to the size of the matrix. Participants explore the definitions and implications of geometric multiplicities, eigenvectors, and the structure of diagonalizable matrices.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks clarification on the meaning of "sum of the geometric multiplicities" and its relevance to proving diagonalizability.
  • Another participant confirms that geometric multiplicity refers to the dimension of the solution space for each eigenvalue and suggests summing these dimensions.
  • A participant questions how to correctly sum the dimensions of the eigenspaces for a 3x3 matrix and whether the total geometric multiplicity must equal the matrix size.
  • One participant proposes that if the sum of geometric multiplicities is less than the size of the matrix, then the matrix cannot be diagonalizable, raising concerns about their assumptions.
  • Another participant elaborates on the dimensionality of eigenspaces, explaining how unique and degenerate eigenvalues contribute to the total geometric multiplicity.
  • A later post presents a matrix representation of diagonalizability and poses questions about the dimensions of eigenspaces and their implications for the existence of a diagonal basis.

Areas of Agreement / Disagreement

Participants express uncertainty about the correct interpretation and application of geometric multiplicities, with multiple viewpoints on how to approach the proof of diagonalizability. There is no consensus on the specific details of the proof or the implications of the geometric multiplicities.

Contextual Notes

Participants discuss various assumptions regarding the dimensions of eigenspaces and the conditions under which a matrix is diagonalizable. The conversation highlights the complexity of these concepts without reaching definitive conclusions.

Bullington
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My professor states that "A matrix is diagonalizable if and only if the sum of the geometric multiplicities of the eigen values equals the size of the matrix". I have to prove this and proofs are my biggest weakness; but, I understand that geometric multiplicites means the dimensions of the solution space for the equation Ax=λx (right?). But what does the "sum of the geometric multiplicities" mean? Could you point me in the right direction, thanks!
 
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Bullington said:
(right?)
geometric multiplicity is the dimension of the solution space of ##\vec{\vec A}\vec x = \lambda_i \vec x## for one ##\lambda_i##. add them up for all ##i## and you get the sum of the geometric multiplicities, which you are asked to prove is equal to the size of A.
 
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How could I add up the dimensions? So for a 3x3 matrix that has three unique eigen vectors would I say that the dimension each of the eigen spaces is 3 and the sum of the geometric multiplicities is 3? Then would I say that A would have to be a square matrix of order 3?
 
Is this in the right direction:
In order for a matrix “A” to be diagonalizable then there is an equation such that P-1AP=D where D is the diagonalized matrix and P is the matrix formed from the Eigenvectors of A and if the sum of the geometric multiplicities is less than the size of A then P will not be invertible? Am I too far off, or did I assume something I shouldn't have?
 
Bullington said:
How could I add up the dimensions? So for a 3x3 matrix that has three unique eigen vectors would I say that the dimension each of the eigen spaces is 3 and the sum of the geometric multiplicities is 3? Then would I say that A would have to be a square matrix of order 3?
No, if an eigenvector ##\vec x## has a unique eigenvalue ##\lambda_x##, all multiples of that vector have the property ##
\vec{\vec A}(p \vec x) = \lambda_x (p\vec x)\ ## (p a real number) so the dimension of the solution space is 1. Three unique eigenvalues let that add up to 3.

If two vectors ##\vec x## and ##\vec y## have the same ##\lambda##, then ##p\vec x + q\vec y## has that too and the solution space for that degenerate eigenvalue has dimension 2. One other plus these 2 adds up to 3.
 
=> A is diagonalizable : ##A \sim \begin{pmatrix}\lambda_1 \text{ Id}_{m_1} & 0 & 0 \\
0 & \ddots & 0\\
0 & 0 & \lambda_p \text{ Id}_{m_p} \end{pmatrix}##. What is ##m_1,...,m_p## ? What is ##m_1 + ... + m_p ## equal to ?

<= Say that matrix A represents an endomorphism on vector space ##E##. You are given that ## \text{dim}(E_{\lambda_1}) + ... + \text{dim}(E_{\lambda_p}) = \text{dim}(E) ##. Can you show that ##E=E_{\lambda_1} \bigoplus ... \bigoplus E_{\lambda_p} ## ? How does this prove that their exists a basis of ##E## in which A is diagonal ?
 

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