Can Disjoint Nullspace and Range Exist if Rank T Equals Rank T^2?

In summary, the conversation discusses an attempt to prove that if the rank of a linear operator T on a finite dimensional vector space V is equivalent to the rank of T^2, then the range and nullspace of T are disjoint. The person has played around with the rank-nullity theorem and a theorem about subspaces, but is unsure how to use the given rank information to their advantage. A small hint is requested.
  • #1
lineintegral1
77
1

Homework Statement



I'm trying to prove the following:
Let V be a finite dimensional vector space and let T be a linear operator on V. Suppose that the rank of T is equivalent to the rank of T^2. Then the range and the nullspace of T are disjoint. 2. The attempt at a solution

I've played around with a few things so far. Clearly, the nullity of T and the nullity of T^2 are the same by the rank-nullity theorem. I'm thinking that the way to start this is to show that the range of T and T^2 are the same. But I'm not sure exactly how to use the given rank information to my advantage.

I was also playing with a theorem that states that, given subspaces W_1 and W_2, then,

dim(W_1) + dim(W_2) = dim(W_1 intersection W_2) + dim(W_1 + W_2).

I was thinking that I could show that the dimension of the intersection is zero since the intersection is zero (and, therefore, the intersection won't have a basis). But again, I'm a little uncertain as to how to use the rank to my advantage.

A small hint would be appreciated. :)

Thanks!
 
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  • #2
assume you had a vector in both the range and nullspace of T, what would happen when operated on again by T?
 

Related to Can Disjoint Nullspace and Range Exist if Rank T Equals Rank T^2?

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another, while preserving the basic structure of the space. This means that the transformation follows the rules of linearity, such as preserving addition and scalar multiplication.

2. How do you prove that a transformation is linear?

To prove that a transformation is linear, you must show that it satisfies the two main properties of linearity: preservation of addition and preservation of scalar multiplication. This can be done by using algebraic manipulation and substitution to show that the transformation holds for all vectors in the vector space.

3. Can a linear transformation map from a higher-dimensional space to a lower-dimensional space?

Yes, a linear transformation can map from a higher-dimensional space to a lower-dimensional space. This is known as a projection, where the transformation maps the higher-dimensional space onto a subspace of the lower-dimensional space.

4. What is the difference between a linear transformation and a nonlinear transformation?

The main difference between a linear transformation and a nonlinear transformation is that a linear transformation follows the rules of linearity, while a nonlinear transformation does not. This means that a linear transformation preserves the basic structure of the vector space, while a nonlinear transformation may distort or alter this structure.

5. How are linear transformations used in real-world applications?

Linear transformations are used in a variety of real-world applications, including computer graphics, image processing, data compression, and data analysis. They are also used in physics and engineering to model and solve problems involving linear systems, such as circuits and mechanical systems.

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