What is the Kernel, Nullity, Range, and Rank of T given a specific matrix A?

  • Thread starter hachi_roku
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In summary: I have no idea what you are talking about. If you mean the matrix A, it is not 0, it is a 3 by 4 matrix. If you mean the matrix with that single column, yes, of course, that is the matrix with every entry 0 so its product with any matrix is 0. But that has nothing to do with the problem.In summary, the given matrix T has a kernel of dimension 1, nullity of 1, range of 3, and rank of 3. The rank-nullity theorem states that the rank and nullity must add up to the dimension of the domain space, which in this case is 4.
  • #1
hachi_roku
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Homework Statement


let T: R^4 --->R^3, where T(v)=A(v) and matrix A is defined by

A = [2 1 -1 1
1 2 0 5
4 -1 1 0
Find kernel of T, nullity of T, range of T and rank of T


Homework Equations





The Attempt at a Solution


ok. ker(T) = Null(A)

[2 1 -1 1][v1]= [0]
[1 2 0 5] [v2]= [0]
[4 -1 1 0][v3] = [0]
[v4]

simplifying using gauss jordan:

1 0 0 1/6 [v1] [0]
0 1 0 29/12 [v2] = [0]
0 0 1 7/4 [v3] [0]
[v4]

v1 + 1/6V4 = 0
V2 + 29/12v4 = 0
v3 + 7/4v4 = 0 so [ -1/6v4]
[-29/12v4] V all real
[-7/4v4]
[v4]

range and rank i got 3

is this right?
 
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  • #2
anyone?
 
  • #3
First, don't get upset if someone doesn't respond within four hours!

Second, it is easy to check for yourself if you have the kernel right: Take A of each of the vectors in the kernel and see if it is 0. Frankly, I don't know what by mean by
"[ -1/6v4]
[-29/12v4] V all real
[-7/4v4]
[v4]"
Do you mean that the kernel is the one dimensional subspace of R4 spanned by the single vector <-1/6, -12/12, -7/4, 1>?

Finally, no, the nullity and range cannot both be 3. By the "rank-nullity theorem", rank and nullity must add to the dimension of the domain space, in this case, 4.
If you are saying that the kernel is spanned by that single vector, then the kernel has dimension 1, so the nullity is 1, and the rank is 4-1= 3.
 
  • #4
yes that's what i meant by writing that answer, so to check this, i take the matrix of that and see if it is zero?


HallsofIvy said:
First, don't get upset if someone doesn't respond within four hours!

Second, it is easy to check for yourself if you have the kernel right: Take A of each of the vectors in the kernel and see if it is 0. Frankly, I don't know what by mean by
"[ -1/6v4]
[-29/12v4] V all real
[-7/4v4]
[v4]"
Do you mean that the kernel is the one dimensional subspace of R4 spanned by the single vector <-1/6, -12/12, -7/4, 1>?

Finally, no, the nullity and range cannot both be 3. By the "rank-nullity theorem", rank and nullity must add to the dimension of the domain space, in this case, 4.
If you are saying that the kernel is spanned by that single vector, then the kernel has dimension 1, so the nullity is 1, and the rank is 4-1= 3.
 
  • #5
Once again, "take this matrix and see if it is 0" makes no sense. Take what matrix and see if what "makes no sense"?
 

1. What is a kernel in the context of T?

A kernel in the context of T refers to the null space of a linear transformation T. It is the set of all vectors that are mapped to the zero vector by T.

2. How do you find the kernel of T?

To find the kernel of T, you can set up and solve a system of equations using the matrix representation of T. The solution to this system will give you the basis for the kernel of T.

3. What is the significance of finding the kernel of T?

Finding the kernel of T allows you to understand the structure and properties of the linear transformation T. It also helps in solving systems of equations and understanding the range of T.

4. Can the kernel of T be empty?

Yes, the kernel of T can be empty if the linear transformation T is one-to-one, meaning each input has a unique output. In this case, the only vector that maps to the zero vector is the zero vector itself.

5. How does the dimension of the kernel relate to the dimension of the range of T?

By the rank-nullity theorem, the dimension of the kernel of T added to the dimension of the range of T will be equal to the dimension of the domain of T. This means that as the dimension of the kernel of T increases, the dimension of the range of T decreases, and vice versa.

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