Solved] Proving Linear Transformation Properties with Linear Independent Sets

In summary: But, if T is not linearly dependent, then v1, ..., vn are linearly independent and so B is a basis for V.]
  • #1
amanda_ou812
48
1

Homework Statement


Let V and W be vector spaces and T: V-> W be linear.
a) Prove that T is one to one if and only if T carries linearly independent subsets of V onto linearly independent subsets of W.
b) Suppose that T is one to one and that S is a subset of V. Prove that S is linearly independent if and only if T(S) is linearly independent.
c) Suppose B = {v1, v2, ..., vn} is a basis for V and T is one to one and onto. Prove that T(B) = {T(v1), ..., T(vn)} is a basis for W.

Homework Equations


1. nullity + rank = dim V
2. T is one to one if and only if N(T) = {0}
3. T is onto implies R(T) = W
4. R(T) = span (T(B))
5. there may be others

The Attempt at a Solution


a) If T is one to one then N(T)) = {0} which implies that nullity = 0. Then we know that rank = dim V. This implies that T is onto (by a theorem in my text). This means that R(T) = W = span (T(B)) where B is a basis for V. So W = a T(v1) + ...+ a (sub n) T(vn) where B = {v1, ..., vn}. HERE is where I think I NEED another step before I can do the conclusion... So this shows that T carries linearly independent subsets of V onto linearly independent subsets of W

If T carries linearly independent subsets of V onto linearly independent subsets of W, then T is onto, which implies that R(T) = W. this implies that dim R(T) = rank = dim W which implies that rank = dim V and therefore T is one to one.

b) I do not have anything formal...just my thought process
So, I know T is one to one, S is a subset of V and S is linearly independent. So T is one to one implies that N(T) = {0} which implies that rank = dim V which implies that T is onto which implies that R(T) = W = span (T(B)) where B is a basis for V. So if S = B then we know that R(T) = W = span (T(S)). I know that S is linearly independent ... but how do i get to T(S) being linearly independent.

For the other way, I know T is one to one, S is a subset of V and T(S) is linearly independent. I know the same stuff as above but I do not know how T(S) linearly independent implies S is linearly independent.

c)
So, same thoughts here. T is one to one implies that N(T) = {0} which implies that rank = dim V. Also, T is onto which implies that R(T) = W = span (T(B)) where B is a basis for V. ok, so how do I show that it is a basis for W?
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  • #2


For c), suppose not. Suppose there is a vector T(vx), which can be expressed in terms of other T(vi). Knowing T is linear, what does that imply?
 
  • #3


Then T is linearly dependent. So there exists lambda's, not all 0, such that the linear combination is 0. Also, if T is linearly dependent than v1, ..., vn are linearly dependent so then B is not a basis for V.
 

1. What is a linear transformation?

A linear transformation is a mathematical operation that maps inputs from one vector space to outputs in another vector space in a linear manner. It follows the properties of linearity, such as preserving scalar multiplication and vector addition.

2. How do you prove linear transformation properties?

In order to prove linear transformation properties, you can use various techniques such as showing that the transformation preserves vector addition and scalar multiplication, or by using matrix representations and showing that the transformation holds true for all vectors in the vector space.

3. What is the importance of using linear independent sets in proving linear transformation properties?

Linear independent sets are important in proving linear transformation properties because they form a basis for the vector space, which means that any vector in the space can be expressed as a linear combination of the basis vectors. This allows for a more concise and efficient way of proving properties without having to test every single vector in the space.

4. What are some common examples of linear transformations?

Some common examples of linear transformations include rotation, scaling, and reflection in two or three-dimensional space. Other examples include differentiation and integration in calculus, and Fourier transforms in signal processing.

5. Can linear transformation properties be applied to any vector space?

Yes, linear transformation properties can be applied to any vector space, as long as the properties of linearity hold true. This includes finite and infinite-dimensional vector spaces, as well as different types of vector spaces such as function spaces and matrix spaces.

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