Eigen Vectors, Geometric Multiplicities and more....

In summary: In order for matrix A to be diagonalizable, there exists 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. To find the basis of E in which A is diagonal, one would need to solve for the Eigenvalues and Eigenvectors of A.
  • #1
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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  • #2
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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  • #3
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?
 
  • #4
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?
 
  • #5
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.
 
  • #6
=> 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 ?
 

FAQ: Eigen Vectors, Geometric Multiplicities and more....

What is the definition of an eigen vector?

An eigen vector is a non-zero vector that, when multiplied by a specific square matrix, results in a scaled version of the original vector. The scaling factor is known as the eigenvalue.

How are eigen vectors and eigen values related?

Eigen vectors and eigen values are closely related, as each eigen vector has a corresponding eigenvalue. The eigenvalue represents the scaling factor of the eigen vector when multiplied by a matrix.

What is the geometric multiplicity of an eigen value?

The geometric multiplicity of an eigen value is the number of linearly independent eigen vectors that correspond to that particular eigenvalue. It is also referred to as the dimension of the eigenspace.

How do you find the eigen vectors and eigen values of a matrix?

To find the eigen vectors and eigen values of a matrix, you first need to find the characteristic polynomial of the matrix. Then, you can use various methods such as the determinant or diagonalization to solve for the eigen values. Once the eigen values are found, you can find the corresponding eigen vectors by solving the system of equations formed by substituting each eigen value into the original matrix equation.

How are eigen vectors used in real-world applications?

Eigen vectors have numerous real-world applications, including image and signal processing, data compression, and machine learning. They are also used in the study of quantum mechanics and population genetics. In these applications, eigen vectors are used to transform and analyze data, identify patterns, and make predictions.

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