Eigenvalues/vectors diagonalization

In summary, if a matrix A has two distinct eigenvalues and the dimension of the eigenvector space for one of them is n-1, then A is diagonalizable. This is because the n-1 independent eigenvectors for the first eigenvalue, plus one eigenvector for the second eigenvalue, form a basis for the vector space. By writing the matrix in this basis, it can be shown to be diagonal with the first eigenvalue appearing n-1 times on the main diagonal and the second eigenvalue appearing once. This can also be deduced by looking at the multiplicity of the eigenvalue in the characteristic polynomial.
  • #1
emyt
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Homework Statement



Suppose that [tex] A \in Mnxn(F) [/tex] has two distinct eigenvalues [tex]\lambda_{1}[/tex] and [tex]\lambda_{2}[/tex] and that [tex] dim(E_{\lambda_{1}}) = n - 1. [/tex] Prove that A is diagonalizable

Homework Equations





The Attempt at a Solution


hmm, I'm not sure.. how would I start this?

thanks
 
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  • #2
If the dimension of the eigenvector space corresponding to eigenvalue [itex]\lambda_1[/itex] is n- 1, then there are n-1 independent eigenvectors corresponding to [itex]\lambda_1[/itex]

That, together with an eigenvector corresponding to eigenvalue [itex]\lambda_2[/itex] gives you n independent vectors (eigenvectors corresponding to distinct eigenvalues are always independent) and form a basis for the vector space. The matrix for the linear transformation, written in that basis, is a diagonal matrix having [itex]\lambda_1[/itex] n-1 times on the main diagonal and [itex]\lambda_2[/itex] once.
 
  • #3
thanks, I was thinking about that too, but for some reason my mind strayed and thought that I would need 2n independent vectors..
I was also looking at a couple of the diagonal "if and only if" theorems. I saw a couple that might've been useful, like:
T is diagonalizable if and only if the multiplicity of[tex]\lambda[/tex] is equal to [tex] dim(E_{\lambda})[/tex].
And now I have to ask: how can you deduce the multiplicity of an eigenvalue without working out the characteristic polynomial and looking at the multiplicity of the factors?
thanks :)
 

FAQ: Eigenvalues/vectors diagonalization

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to describe the behavior of a linear transformation. Eigenvalues are the scalars that represent the amount by which an eigenvector is stretched or compressed. Eigenvectors are the vectors that remain in the same direction after a linear transformation is applied.

How is diagonalization related to eigenvalues and eigenvectors?

Diagonalization is a process that involves finding a matrix that is similar to a given square matrix, but has all zeros except for the main diagonal. This process is only possible for square matrices that have a full set of eigenvectors. The eigenvalues of the original matrix will appear on the main diagonal of the diagonalized matrix.

Why is diagonalization useful?

Diagonalization is useful because it allows us to simplify calculations involving matrix multiplication and powers. It also helps us to understand the behavior of a linear transformation and to identify important features of a matrix, such as the dominant eigenvector.

How do you find the eigenvalues and eigenvectors of a matrix?

The eigenvalues of a matrix can be found by solving the characteristic equation, which is det(A-λI)=0, where A is the matrix, λ is the eigenvalue, and I is the identity matrix. The eigenvectors can then be found by plugging the eigenvalues into the equation (A-λI)x=0 and solving for x.

Can any matrix be diagonalized?

No, not all matrices can be diagonalized. A matrix can only be diagonalized if it has a full set of linearly independent eigenvectors. In other words, if the matrix is not diagonalizable, it means that some eigenvectors are missing and the matrix cannot be simplified to a diagonal form.

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