Prove one to one using rank-nullity theorem

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In summary, the problem states that given two finite dimensional vector spaces V and W, with dim(V)≤ dim(W), we need to prove that there exists a one-to-one linear transformation T : V -> W. This means that we need to define a linear transformation from V to W and show that it is one-to-one. We can start by choosing some basis for both V and W, and then using the fact that dim(W) is greater than or equal to dim(V), we can show that any basis of W must have at least as many vectors as the basis of V. This allows us to define a linear transformation T that maps each vector in V to a unique vector in W, making it one-to-one.
  • #1
Hwng10
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Homework Statement



Let V and W be finite dimensional vector spaces.Given dim(V)≤ dim(W)
, prove that there exists a one-to-one linear transformation T : V -> W .

Homework Equations





The Attempt at a Solution


What I want to prove here is to show that nullity=0
dim(V) ≤ dim (W)
dim(N(T)) + dim(R(T)) ≤ dim(W)
 
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  • #2
You can't prove that the nullity of T until after you have T! How are you defining T?
 
  • #3
I really have no idea no how to prove this question . Do I need to prove it for general T or I just need to find an example which satisfies all the properties above ??
 
  • #4
I suggest you go back and reread the problem! "prove that there exists"... First define an obvious linear transformation from V to W, then show that it is one-to-one.

Here's a simple special case: suppose dim(V)= 2, dim(W)= 3. Then, choosing some basis for both V and W, you can write any vector in V as <x, y> and any vector in W as <a, b, c>. Okay, what can you say about T(<x, y>)= <x, y, 0>?
 
  • #5
HallsofIvy said:
I suggest you go back and reread the problem! "prove that there exists"... First define an obvious linear transformation from V to W, then show that it is one-to-one.

Here's a simple special case: suppose dim(V)= 2, dim(W)= 3. Then, choosing some basis for both V and W, you can write any vector in V as <x, y> and any vector in W as <a, b, c>. Okay, what can you say about T(<x, y>)= <x, y, 0>?

hint 2: if dim(V) = n, we have a basis {v1,v2,...,vn}.

since dim(W) ≥ dim(V), what can you say about any basis of W?
 

1. What is the rank-nullity theorem?

The rank-nullity theorem is a fundamental theorem in linear algebra that states that the rank of a matrix plus the nullity of the matrix is equal to the number of columns in the matrix. In other words, it relates the dimensions of the range and null space of a linear transformation.

2. How is the rank-nullity theorem used to prove one-to-one?

The rank-nullity theorem can be used to prove that a linear transformation is one-to-one by showing that the nullity of the transformation is equal to 0. This means that the null space of the transformation is empty, indicating that there are no non-zero vectors that get mapped to the zero vector, and therefore the transformation is one-to-one.

3. Can the rank-nullity theorem be applied to non-linear transformations?

No, the rank-nullity theorem only applies to linear transformations. Non-linear transformations do not have a well-defined range and null space, making the theorem inapplicable.

4. How does the dimension of the null space affect the rank-nullity theorem?

The dimension of the null space, or nullity, is an important factor in the rank-nullity theorem. It represents the number of linearly independent vectors that get mapped to the zero vector by the transformation. The theorem states that the rank plus the nullity equals the number of columns, so as the nullity increases, the rank must decrease to maintain this equality.

5. Are there any exceptions to the rank-nullity theorem?

Yes, there are a few exceptions to the rank-nullity theorem. One exception is when the matrix is not square, in which case the rank and nullity may not add up to the number of columns. Another exception is when the matrix is not full rank, meaning it has linearly dependent columns, in which case the rank will be less than the number of columns and the nullity will be greater than 0.

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