Question about Linear Operator's Image and Kernel

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Homework Help Overview

The discussion revolves around a linear operator T: V → V and the implications of the condition Ker(T^2) = Ker(T) on the images of T and T^2. Participants are exploring the relationship between the kernel and image of linear operators in the context of finite and infinite dimensional vector spaces.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • Participants discuss proving the inclusions Im(T) ⊆ Im(T^2) and Im(T) ⊇ Im(T^2) separately. There is mention of using the rank-nullity theorem to relate the dimensions of the images and the implications of equal dimensions for vector spaces.

Discussion Status

Some participants have made progress in showing one inclusion and are considering the implications of dimensions for vector spaces. Questions about the applicability of the results in finite versus infinite dimensional cases have been raised, indicating an ongoing exploration of the problem.

Contextual Notes

It is noted that the vector space V is finite dimensional, which may influence the validity of certain arguments and theorems discussed.

canis89
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Homework Statement



If T:V\rightarrow V is linear, then Ker(T^2)=Ker(T) implies Im(T^2)=Im(T).

Homework Equations



Let T:V\rightarrow V be a linear operator such that \forall x\in V,

T^2(x)=0\Rightarrow T(x)=0 (Ker(T^2)=Ker(T)).​

Prove that \forall x\in V, \exists u\in V\ni T(x)=T^2(u) (Im(T^2)=Im(T)).

The Attempt at a Solution



Any clue on where I should start? I'm really stuck at this problem and have been thinking about it for the past two days. The problem is I don't know how to use the assumption Ker(T)=Ker(T^2) .
 
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Try to prove the inclusions \text{im}(T) \subseteq \text{im}(T^2) and \text{im}(T) \supseteq \text{im}(T^2) separately. One inclusion should be easy and the other will require slightly more work.

There is a theorem from which this result is immediate, but you might not have seen it before...
 
I've managed to show that Im(T^2)\subseteq Im(T). The other direction is my problem. But somehow, it has struck me just now that maybe I can use the rank-nullity theorem to show it. The theorem implies that dim(Im(T^2))=dim(Im(T)). And since both of them are vector spaces, it suffices to show that two vector spaces with the same vector addition and scalar multiplication and also dimension are indeed equal.

Furthermore, since Im(T^2)\subseteq Im(T), it suffices to show that a subspace with the same dimension as its superspace (I don't know the correct term) is indeed equal to the latter. This leads to the question: does the set of linearly independent vectors (the basis) of the subspace span the superspace?
 
I have found the answer to the previous question. Let A be the basis of Im(T^2). IF if there exists v\in Im(T)-Span(A), then v is linearly independent from A and hence the number of linear independent vectors of Im(T^2) is plus one than the maximum number (the number of basis vectors). This is a contradiction. Hence, Im(T)=Span(A)=Im(T^2). Please let me know if I've made some mistakes.
 
Yes, I forgot to mention, V is finite dimensional. Oh, I'm not familiar with the theorem as I'm also not familiar with algebraic structures. Do you think that the problem can also be proven in the infinite dimensional case?
 
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