Linear transformation proof

Is the proof complete now?Yes, the proof is complete. In summary, we have proven that the set T^{-1}(U) is a subspace of V by showing that it contains the zero vector, is closed under addition, and is closed under scalar multiplication. Additionally, when U is equal to the zero vector, T^{-1}(U) is equivalent to the kernel of T.
  • #1
trojansc82
60
0

Homework Statement


Let T: V --> W be a linear transformation and let U be a subspace of W. Prove that the set T-1 (U) = {v E V: T (v) E U} is a subspace of V. What is T-1 if U = {0}?


Homework Equations





The Attempt at a Solution



Since U is a subspace, k(v) = ku. Also, if u and v are in U, u + v lies in U. Therefore, any inverse linear transformation of a vector within u will lie in the subspace U.
 
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  • #2
There are three things to prove about [tex]T^{-1}(U)[/tex], can you tell me what those things are?
 
  • #3
1. T-1 a one to one transformation.

2. Closure under addition

3. Closure under scalar multiplication
 
  • #4
trojansc82 said:
1. T-1 a one to one transformation.

2. Closure under addition

3. Closure under scalar multiplication

1 isn't always true. So, that is incorrect. What I was looking for was:

1) It contains the zero vector

2&3 are correct.

So, start with (1), try to prove that [tex]0\in T^{-1}(U)[/tex].
 
  • #5
For vector u in T-1(U), 0u = 0. (zero vector property).
 
  • #6
trojansc82 said:
For vector u in T-1(U), 0u = 0. (zero vector property).

Correct, but I don't see how that has to do with anything. By definition, we have that

[tex]T^{-1}(U)=\{v\in V~\vert~T(v)\in U\}[/tex]

Thus, we have [tex]v\in T^{-1}(U)~\Leftrightarrow~T(v)\in U[/tex]

So, to prove that [tex]0\in T^{-1}(U)[/tex], the statement above shows that it is sufficient to show that [tex]T(0)\in U[/tex]. Do you see why this is the case?
 
  • #7
How would I proceed with scalar multiplication and closure under addition? The same process?
 
  • #8
Yes, it's exactly the same thing!
 
  • #9
trojansc82 said:

Homework Statement


Let T: V --> W be a linear transformation and let U be a subspace of W. Prove that the set T-1 (U) = {v E V: T (v) E U} is a subspace of V. What is T-1 if U = {0}?
The question is asking you to determine the set of all u such that T(u)= 0. There is a specific name for that set!
 
  • #10
HallsofIvy said:
The question is asking you to determine the set of all u such that T(u)= 0. There is a specific name for that set!

Yeah, the kernel.
 

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another in a way that preserves the structure of the original vector space. This means that the transformation preserves addition, scalar multiplication, and the zero vector.

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

To prove that a transformation is linear, you must show that it satisfies two properties: preservation of addition and preservation of scalar multiplication. This means that for any two vectors u and v, and any scalar c, the transformation of their sum (T(u+v)) is equal to the sum of the individual transformations (T(u) + T(v)), and the transformation of a scalar multiple of a vector (T(cu)) is equal to the scalar multiple of the transformation (cT(u)).

3. What is the importance of linear transformation proofs?

Linear transformation proofs are important because they allow us to mathematically verify that a function preserves the properties of a vector space. This is crucial in applications such as geometry, physics, and computer graphics where preserving the structure of a vector space is essential.

4. Can a linear transformation have a negative determinant?

No, a linear transformation cannot have a negative determinant. The determinant of a linear transformation is a measure of how much the transformation changes the volume of a vector space. Since a negative determinant would indicate a negative volume, which is not possible, the determinant must always be positive.

5. How do you use matrices to represent a linear transformation?

Matrices can be used to represent linear transformations by representing the transformation as a multiplication of a matrix and a vector. The columns of the matrix represent the images of the basis vectors in the original vector space, and the resulting vector is the image of any vector in the original space. This allows for easier computation and manipulation of linear transformations.

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