Depth of an eigen value (generalized eigenspaces)

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In summary, the depth of an eigenvalue is the number of times it appears in the generalized eigenspace. It is equal to its algebraic multiplicity, which is the number of times it appears as a root of the characteristic polynomial. The depth of an eigenvalue can be greater than its algebraic multiplicity if there are repeated eigenvalues in the generalized eigenspace. It does not affect its geometric multiplicity, which is the dimension of its associated eigenspace. In matrix diagonalization, the depth of an eigenvalue determines the number of Jordan blocks associated with that eigenvalue in the diagonalized matrix.
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Doom of Doom
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Let [tex]A[/tex] be a matrix and [tex]\mu[/tex] be an eigenvalue of that matrix. Suppose that for some [tex]k[/tex], [tex]\tex{ker}\left(A-\mu I\right)^k=\tex{ker}\left(A-\mu I\right)^{k+1}[/tex]. Then show that [tex]\tex{ker}\left(A-\mu I\right)^{k+r}=\tex{ker}\left(A-\mu I\right)^{k+r+1}[/tex] for all [tex]r\geq0[/tex].
 
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What have you tried? Do you know what the kernel of a matrix is?
 
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Important point: for any matrix A, A0= 0!
 

Related to Depth of an eigen value (generalized eigenspaces)

What is the depth of an eigenvalue?

The depth of an eigenvalue is the number of times it appears in the generalized eigenspace.

How is the depth of an eigenvalue related to its multiplicity?

The depth of an eigenvalue is equal to its algebraic multiplicity, which is the number of times it appears as a root of the characteristic polynomial.

Can the depth of an eigenvalue be greater than its algebraic multiplicity?

Yes, the depth of an eigenvalue can be greater than its algebraic multiplicity if there are repeated eigenvalues in the generalized eigenspace.

How does the depth of an eigenvalue affect its geometric multiplicity?

The depth of an eigenvalue does not affect its geometric multiplicity, which is the dimension of its associated eigenspace.

What is the significance of the depth of an eigenvalue in matrix diagonalization?

The depth of an eigenvalue determines the number of Jordan blocks associated with that eigenvalue in the diagonalized matrix.

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