Please check my proof (generalized eigenspaces)

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In summary, the conversation discusses a proof involving a linear operator T on a vector space V and an eigenvalue λ. It is shown that if the rank of ((T-λI)m) is equal to the rank of ((T-λI)m+1) for some integer m, then the null space of (T-λI)m is equal to the kernel of (T-λI)m+1. The conversation also mentions that this is part 4 of a 6 part proof and discusses the relationship between Kλ and N((T-λI)m. The attempt at a solution involves proving that if x is in Kλ, then it is also in N((T-λI)m, and vice versa.
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bennyska
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Homework Statement


let T be a linear operator on V, and let λ be an eigenvalue of T. prove that if rank((T-λI)m = rank((T-λI)m+1 for some integer m, then Kλ = N((T-λI)m.


Homework Equations


this is part 4 of a 6 part proof, and in the earlier stages, i showed if rank((T-λI)m = rank((T-λI)m+1 for some integer m, then N((T-λI)m = N((T-λI)m+1.


The Attempt at a Solution


let x be in Kλ. by previous result, N((T-λI)m = N((T-λI)k for k≥ m. x is in Kλ, so x is in N((T-λI)p for some integer p. if p≥m, then x is in N((T-λI)m. if p<m, then m = p+t for some t. then (T-λI)p+t(x) = (T-λI)t(T-λI)p(x) = (T-λI)t(0)=0, so x is in N((T-λI)m.
let y be in N((T-λI)m. then y is in N((T-λI)p for some integer p, so y in in Kλ, and the two sets are equal.
 
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  • #2
well, i see a mistake already, in the "if p≥m" part...
 
  • #3
can i just say, since (T-λI)p(x)=0, then x is N((T-λI)m if m=p?
 

1. What is a generalized eigenspace?

A generalized eigenspace is a vector space that contains all the generalized eigenvectors corresponding to a specific eigenvalue of a linear transformation. It is a subspace of the original vector space and can be thought of as the "extended" eigenspace for eigenvalues with algebraic multiplicity greater than one.

2. How is a generalized eigenspace calculated?

A generalized eigenspace is calculated by finding the null space of the generalized eigenvector matrix. This matrix is created by raising the original matrix to the power of its algebraic multiplicity and subtracting the identity matrix multiplied by the scalar eigenvalue.

3. What is the significance of generalized eigenspaces?

Generalized eigenspaces are important in understanding the behavior of a linear transformation and its associated eigenvalues. They can be used to generalize the concept of eigenvectors for matrices that do not have a complete set of linearly independent eigenvectors, and can also help in finding the Jordan canonical form of a matrix.

4. Can a generalized eigenspace be empty?

Yes, it is possible for a generalized eigenspace to be empty. This occurs when the algebraic multiplicity of an eigenvalue is greater than the number of linearly independent generalized eigenvectors corresponding to that eigenvalue.

5. How are generalized eigenspaces used in applications?

Generalized eigenspaces are used in a variety of applications in physics, engineering, and other fields. They can be used to analyze the behavior of linear systems, calculate the stability of a system, and find solutions to differential equations. They are also used in applications such as image processing and signal analysis.

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