Diagonalizability of matrix

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In this case, since we have two distinct eigenvalues and dim(E^{\lambda_1}) = n-1, the dimension of V must be n, so A is diagonalizable. In summary, we can conclude that if a matrix A has two distinct eigenvalues and dim(E^{\lambda_1}) = n-1, then A is diagonalizable.
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Homework Statement


Suppose the A [tex]\in[/tex] Mn X n(F) has two distinct eigenvalues, [tex]\lambda[/tex]1 and [tex]\lambda[/tex]2, and that dim(E[tex]\lambda[/tex]1) = n -1. Prove A is diagonalizable.

Homework Equations


The Attempt at a Solution



1. The charac poly clearly splits because we have eigenvalues.
2. need to show m = dim (E).

Ok, we are given that dim(E[tex]\lambda[/tex]1) = n - 1

we know multiplicity has to be 1 [tex]\leq[/tex] dim(E[tex]\lambda[/tex]1) [tex]\leq[/tex] m.

so: 1 [tex]\leq[/tex] n - 1 [tex]\leq[/tex] m.

But I am stuck now, not sure how to show that m = dim(E[tex]\lambda[/tex])
 
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  • #2


We're told that [tex]dimE^{\lambda_1}[/tex] is [tex]n-1[/tex]. What is [tex]dimE^{\lambda_2}[/tex] then, and what can you say about [tex]E^{\lambda_1} \oplus E^{\lambda_2}[/tex]?
 
  • #3


Office_Shredder said:
We're told that [tex]dimE^{\lambda_1}[/tex] is [tex]n-1[/tex]. What is [tex]dimE^{\lambda_2}[/tex] then, and what can you say about [tex]E^{\lambda_1} \oplus E^{\lambda_2}[/tex]?

[tex]dimE^{\lambda_2}[/tex] = 1 then right?

[tex]E^{\lambda_1} \oplus E^{\lambda_2}[/tex] = V?
 
  • #4


A linear operator T on a finite-dimensional vector space V is diagonalizable iff V is the direct sum of eigenspaces of T.
 

1. What does it mean for a matrix to be diagonalizable?

Diagonalizability refers to the property of a square matrix where it can be transformed into a diagonal matrix through a similarity transformation. This means that the matrix's eigenvalues and eigenvectors can be used to create a diagonal matrix that is equivalent to the original matrix.

2. How do I know if a matrix is diagonalizable?

A matrix is diagonalizable if it has n linearly independent eigenvectors, where n is the size of the matrix. This means that the matrix must have n distinct eigenvalues in order to be diagonalizable. Additionally, if the matrix is symmetric, it is always diagonalizable.

3. Can a non-square matrix be diagonalizable?

No, diagonalizability only applies to square matrices. A non-square matrix cannot have eigenvalues or eigenvectors, which are essential for diagonalizability.

4. What are the benefits of diagonalizing a matrix?

Diagonalizing a matrix can make it easier to perform calculations such as matrix multiplication and finding powers of the matrix. It also allows for simpler analysis of the matrix's behavior and properties.

5. How can I diagonalize a matrix?

The process of diagonalizing a matrix involves finding its eigenvalues and eigenvectors, which can be done through various methods such as the characteristic polynomial or Gaussian elimination. Once the eigenvalues and eigenvectors are found, they can be used to create a diagonal matrix through a similarity transformation.

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