Question about Linear Operator's Image and Kernel

In summary, the conversation discusses a proof for the statement "If T:V\rightarrow V is linear, then Ker(T^2)=Ker(T) implies Im(T^2)=Im(T)" and suggests using the rank-nullity theorem to show that Im(T^2)=Im(T) in the finite dimensional case. The conversation also mentions a theorem (the first isomorphism theorem) that can be used to prove the statement in the infinite dimensional case. The conversation concludes with the question of whether the statement can also be proven in the infinite dimensional case using the same strategy.
  • #1
canis89
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Homework Statement



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

Homework Equations



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

[itex]T^2(x)=0\Rightarrow T(x)=0[/itex] (Ker(T^2)=Ker(T)).​

Prove that [itex]\forall x\in V, \exists u\in V\ni T(x)=T^2(u)[/itex] (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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  • #2
Try to prove the inclusions [itex] \text{im}(T) \subseteq \text{im}(T^2)[/itex] and [itex] \text{im}(T) \supseteq \text{im}(T^2)[/itex] 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...
 
  • #3
I've managed to show that [itex]Im(T^2)\subseteq Im(T)[/itex]. 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 [itex] dim(Im(T^2))=dim(Im(T))[/itex]. 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 [itex]Im(T^2)\subseteq Im(T)[/itex], 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?
 
  • #4
I have found the answer to the previous question. Let A be the basis of [itex]Im(T^2)[/itex]. IF if there exists [itex]v\in Im(T)-Span(A)[/itex], then v is linearly independent from A and hence the number of linear independent vectors of [itex]Im(T^2)[/itex] is plus one than the maximum number (the number of basis vectors). This is a contradiction. Hence, [itex]Im(T)=Span(A)=Im(T^2)[/itex]. Please let me know if I've made some mistakes.
 
  • #6
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?
 
Last edited:

Related to Question about Linear Operator's Image and Kernel

1. What is the definition of a linear operator?

A linear operator is a mathematical function that maps one vector space to another, preserving the algebraic operations of addition and scalar multiplication. In other words, it is a function that preserves the structure of vector spaces.

2. What is the image of a linear operator?

The image of a linear operator is the set of all vectors in the target space that can be reached by applying the operator to any vector in the original space. It represents the range of possible outputs of the operator.

3. How is the kernel of a linear operator defined?

The kernel of a linear operator is the set of all vectors in the original space that are mapped to the zero vector in the target space. In other words, it is the set of all inputs that result in an output of zero.

4. What is the relationship between the image and kernel of a linear operator?

The image and kernel of a linear operator are complementary subspaces. This means that every vector in the original space can be uniquely decomposed into a sum of a vector in the kernel and a vector in the image of the operator.

5. How can the image and kernel of a linear operator be used in practical applications?

The image and kernel of a linear operator have many practical applications in fields such as engineering, physics, and computer science. They can be used to solve systems of linear equations, analyze data, and model physical systems. Additionally, understanding the image and kernel of an operator can help in finding efficient solutions to problems and optimizing algorithms.

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