Matrix representation of linear transformation

In summary: In other words, if a general vector, v, in V is represented in B by \begin{bmatrix}a \\ d \\ h\end{bmatrix} then Tv= aT(b1) + dT(b2) + hT(b3) where T(b1), T(b2), T(b3) are the columns of [T]C B. So if vector w in W is in the range of T, then it can be written as a linear combination of the columns of [T]C B, and therefore its representation in C is in the column space of [T]C B. In summary, the vector v is in the kernel of the linear map T if and only if
  • #1
toni07
25
0
Let V and W be two finite-dimensional vector spaces over the field F. Let B be a basis of V, and let C be a basis of W. For any v 2 V write [v]B for the coordinate vector of v with respect to B, and similarly [w]C for w in W. Let T : V -> W be a linear map, and write [T]C B for the matrix representing T with respect to B and C.
a) Prove that a vector v in V is in the kernel of the linear map T if and only if the vector [v]B in F^dimV is in the nullspace of the matrix [T]C B.
b) Prove that a vector w in W is in the range of the linear map T if and only if the vector [w]C in F^dimW is in the column space of the matrix [T]C B.
I don't know how to go about this question, any guidelines would really help.
 
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  • #2
Given an ordered basis, B, for V and an ordered basis, C, for W. We write the matrix representation for T: V-> W by applying T to each vector in B, in turn, then writing the result as a linear combination of the vectors in C. The coefficients form the columns of [T]C B. That is because the first vector in B would be represented by [itex]\begin{bmatrix}1 \\ 0 \\ 0 \\ ... \\ 0\end{bmatrix}[/itex], the second by [itex]\begin{bmatrix}0 \\ 1 \\ 0 \\ ... \\ 0\end{bmatrix}[/itex], etc. Then
[tex]\begin{bmatrix}a & b & c \\ d & e & f\\ h & i & j\end{bmatrix}\begin{bmatrix}1 \\ 0 \\ 0 \end{bmatrix}= \begin{bmatrix}a \\ d \\ h\end{bmatrix}[/tex]
That is, multiplying the matrix representation of T by the matrix representation of each basis vector in B gives a column of the matrix, the matrix representation of the corresponding basis vector in C.

A vector, v, is in the kernel of T if and only if Tv= 0 which would be written, in terms of basis C, as the number 0 times each vector in C. That would be represented by the matrix [itex]\begin{bmatrix} 0 \\ 0 \\ 0 \end{bmatrix}[/itex] so that the representation of v in B is in the nullspace of [T]C B.

Similarly, since the columns of [T]C B give the coefficients of the linear combinations of basis vectors in C, there is a one to one correspondence between the range of T and the linear combinations of the columns.
 

1. What is a matrix representation of a linear transformation?

A matrix representation of a linear transformation is a way to represent a linear transformation using a matrix. This matrix can be used to perform the same transformation as the original linear transformation.

2. How is a matrix representation of a linear transformation different from the original transformation?

A matrix representation of a linear transformation is a more concise and efficient way to perform the transformation. It allows for easy computation and manipulation, and can be easily applied to multiple vectors at once.

3. How do you find the matrix representation of a linear transformation?

To find the matrix representation of a linear transformation, you first need to choose a basis for both the domain and the codomain of the transformation. Then, you can apply the transformation to each vector in the basis of the domain and write the resulting vectors as columns in a matrix. This matrix will be the matrix representation of the linear transformation.

4. Can any linear transformation be represented by a matrix?

Yes, any linear transformation can be represented by a matrix. However, the size of the matrix may vary depending on the dimensions of the domain and codomain of the transformation.

5. What are the benefits of using a matrix representation of a linear transformation?

Using a matrix representation of a linear transformation allows for easier computation and manipulation, as well as the ability to apply the transformation to multiple vectors at once. It also allows for a more concise representation of the transformation, making it easier to understand and work with.

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