Another Linear Algebra proof about linear transformations

In summary: Then conclude that A spans Im(T).In summary, if β = {v1, ..., vm} is a basis of V, then { T(v1), ..., T(vm) } spans the image of T because all vectors in Im(T) can be written as a linear combination of the vectors in A.
  • #1
brushman
113
1

Homework Statement


Given:
T is a linear transformation from V -> W and the dim(V) = n and dim(W) = m

Prove:
If β = {v1, ..., vm} is a basis of V, then { T(v1), ..., T(vm) } spans the image of T.
NOTE: because of bad hand writing I can't tell if the bold is suppose to be an 'm' or an 'n'. I think 'm' because that makes more sense to me.

The Attempt at a Solution



Let A = { T(v1), ..., T(vm) } .

We must show that the vectors in A are L.I., and that the dim(A) is m.

If we show the vectors of A are L.I. then since there are m vectors we know the dimension is m.

Then there must be only the trivial solution to c1T(v1) + ... + cmT(vm) = 0 .
Or, by linearity, T(c1v1 + ... + cmvm) = 0 .

Note: I don't think I can jump straight to the next step. (in a similar thread of mine I did so by utilizing the fact that the null space is 0).

Since the vectors v1...vm form a basis, they are L.I. and only the trivial solution exists.

Therefore span(A) = Im(T).
 
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  • #2
brushman said:

Homework Statement


Given:
T is a linear transformation from V -> W and the dim(V) = n and dim(W) = m

Prove:
If β = {v1, ..., vm} is a basis of V, then { T(v1), ..., T(vm) } spans the image of T.
NOTE: because of bad hand writing I can't tell if the bold is suppose to be an 'm' or an 'n'. I think 'm' because that makes more sense to me.
But it doesn't make sense to me. It's given that dim(V) = n, so any basis for V must have n vectors. So β = {v1, ..., vn}.

From this, the set of image vectors must be { T(v1), ..., T(vn) }
brushman said:

The Attempt at a Solution



Let A = { T(v1), ..., T(vm) } .

We must show that the vectors in A are L.I., and that the dim(A) is m.

If we show the vectors of A are L.I. then since there are m vectors we know the dimension is m.

Then there must be only the trivial solution to c1T(v1) + ... + cmT(vm) = 0 .
Or, by linearity, T(c1v1 + ... + cmvm) = 0 .

Note: I don't think I can jump straight to the next step. (in a similar thread of mine I did so by utilizing the fact that the null space is 0).

Since the vectors v1...vm form a basis, they are L.I. and only the trivial solution exists.

Therefore span(A) = Im(T).
 
  • #3
brushman said:

Homework Statement


Given:
T is a linear transformation from V -> W and the dim(V) = n and dim(W) = m

Prove:
If β = {v1, ..., vm} is a basis of V, then { T(v1), ..., T(vm) } spans the image of T.
NOTE: because of bad hand writing I can't tell if the bold is suppose to be an 'm' or an 'n'. I think 'm' because that makes more sense to me.

The Attempt at a Solution



Let A = { T(v1), ..., T(vm) } .

We must show that the vectors in A are L.I., and that the dim(A) is m.
The problem isn't asking you to show that A is a basis for W. It's asking you to show A spans Im(T). The vectors in A may not, in fact, be independent.
 
  • #4
Thanks Mark and vela. How does this look:Let A = { T(v1), ..., T(vm) } and β = {v1, ..., vn}.

Since β is a basis of V, all vectors in V can be written as v = c1v1 + ... + cnvn.

Then T(v) = T(c1v1 + ... + cnvn) = c1 T(v1) + ... + cn T(vn).

Because the Im(T) = T(v), and T(v) can be written as a linear combination of the elements of A, A spans Im(T).
 
  • #5
That's the basic idea. I'd probably slightly reorder it: start the proof with "Let [itex]w\in \mathrm{Im}(T)[/itex]" and show that this implies that w can be written as a linear combination of the vectors in A.
 

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 basic algebraic structure of the original space. In simpler terms, it is a function that takes in a vector and outputs another vector, while maintaining the properties of linearity such as scaling and addition.

2. What is the significance of proving a linear transformation?

Proving a linear transformation is important in mathematics because it provides a rigorous and formal way to understand and manipulate vector spaces. It also allows us to make logical deductions about the properties and behaviors of linear transformations, which can then be applied to real-world problems in fields such as physics, engineering, and computer science.

3. How is a linear transformation represented mathematically?

A linear transformation can be represented using a matrix, which is a rectangular array of numbers. The input vector is multiplied by this matrix to produce the output vector. Alternatively, it can also be represented using a set of linear equations, where each equation represents the relationship between the input and output vectors.

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

The main difference between a linear and non-linear transformation is that a linear transformation preserves the properties of linearity, while a non-linear transformation does not. This means that the output of a linear transformation can always be predicted from the input using simple scaling and addition, whereas the output of a non-linear transformation cannot be predicted in the same way.

5. Can a linear transformation have a negative determinant?

Yes, a linear transformation can have a negative determinant. The determinant is a measure of how much the transformation stretches or compresses space. A negative determinant indicates that the transformation includes a reflection or a flip, which is a valid linear transformation. However, a negative determinant can also indicate an error in the calculations, so it is important to double-check the results in such cases.

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