Kernel and image of linear transformation

In summary, for the linear transformation T: R4 --> R3 defined by TA: v --> Av, the basis for the Kernel of TA is the set of all vectors v in R4 such that Tv = 0, and the basis for the Image of TA is the set of all vectors in R3 that can be obtained through the multiplication by A. To find a basis for the Kernel, we can solve the equation Av = 0.
  • #1
Locoism
81
0

Homework Statement


For the linear transformation T: R4 --> R3 defined by TA: v -->Av
find a basis for the Kernel of TA and for the Image of of TA where A is
2 4 6 2
1 3 -4 1
4 10 -2 4

Homework Equations



Let v =
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4

The Attempt at a Solution


so v is a 4x3 matrix, and Ker(T) would just be the solution for Av = 0.
I was unsure as to what the Image would be give by. Is it the matrix
2a1+4b1+6c1+2d1, 2a2+4b2+6c2+2d2, 2a3+4b3+6c3+2d3,
1a1+3b1-4c1+d1, ... etc

(just the general solution of the multiplication)
Which generalizes to
2 0 2
0 1 2
so the basis is [1, 2, -1]

How would I find a basis for the Kernel?
 
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  • #2
Locoism said:

Homework Statement


For the linear transformation T: R4 --> R3 defined by TA: v -->Av
find a basis for the Kernel of TA and for the Image of of TA where A is
2 4 6 2
1 3 -4 1
4 10 -2 4


Homework Equations



Let v =
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
You're really heading down the wrong path here. v is a vector in R4.
Locoism said:

The Attempt at a Solution


so v is a 4x3 matrix, and Ker(T) would just be the solution for Av = 0.
Ker(T) is the set of all vectors v in R4 such that Tv = 0.
Locoism said:
I was unsure as to what the Image would be give by. Is it the matrix
2a1+4b1+6c1+2d1, 2a2+4b2+6c2+2d2, 2a3+4b3+6c3+2d3,
1a1+3b1-4c1+d1, ... etc

(just the general solution of the multiplication)
Which generalizes to
2 0 2
0 1 2
so the basis is [1, 2, -1]

How would I find a basis for the Kernel?
 

1. What is the kernel of a linear transformation?

The kernel of a linear transformation is the set of all input vectors that are mapped to the zero vector in the output space. In other words, it is the collection of all inputs that result in a output of zero.

2. How is the kernel related to the image of a linear transformation?

The kernel and the image of a linear transformation are complementary subspaces. The image is the set of all output vectors that are actually reached by the transformation, while the kernel is the set of all input vectors that are not mapped to the image.

3. How can the kernel and image be used to determine if a linear transformation is one-to-one or onto?

A linear transformation is one-to-one if and only if its kernel contains only the zero vector. This means that no two distinct input vectors are mapped to the same output vector. A linear transformation is onto if and only if its image is equal to its output space. This means that every vector in the output space can be reached by at least one input vector.

4. Is the kernel of a linear transformation always a subspace?

Yes, the kernel of a linear transformation is always a subspace. This is because it contains the zero vector, is closed under vector addition, and is closed under scalar multiplication.

5. Can the kernel and image of a linear transformation be used to determine the rank and nullity of the transformation?

Yes, the rank of a linear transformation is equal to the dimension of the image, while the nullity is equal to the dimension of the kernel. Therefore, by finding the dimensions of the kernel and image, we can determine the rank and nullity of the transformation.

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