Finding a basis for the Kernel of T

In summary, the kernel of the map T is the set of all 2 x 2 matrices whose entries add to 0, and its dimension is 3.
  • #1
mmcgirr4
1
0

Homework Statement



So the question is a map T: R^2x2 ---> R^2x2 by T(A) = BAB, where B = (1 1)
(1 1)

so i made A = (a c) and T(A) = ((a+b) + (c+d) (a+b) + (c+d))
(b d) ((a+b) + (c+d) (a+b) + (c+d))

now it asks Find a basis for the kernel of T and compute the dimension of the kernel T.



Homework Equations





The Attempt at a Solution



This is what I have, but I am not quite sure its right.

ker(T) = {VεR^2x2 : T (V) = 0(vector) R^2x2}

= {(a c) : [(a+b) + (c+d) (a+b) + (c+d)] = [0 0] }
{(b d) [a+b) + (c+d) (a+b) + (c+d)] [0 0] }

then

a = -b -c -d
b = -a-c-d
c = -a-b-d
d = -a-b-c

therefor the Ker(T) = {(a c) : a, b, c, d ε R^2x2}
{(b d) }
 
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  • #2
mmcgirr4 said:

Homework Statement



So the question is a map T: R^2x2 ---> R^2x2 by T(A) = BAB, where B = (1 1)
(1 1)

so i made A = (a c) and T(A) = ((a+b) + (c+d) (a+b) + (c+d))
(b d) ((a+b) + (c+d) (a+b) + (c+d))

now it asks Find a basis for the kernel of T and compute the dimension of the kernel T.



Homework Equations





The Attempt at a Solution



This is what I have, but I am not quite sure its right.

ker(T) = {VεR^2x2 : T (V) = 0(vector) R^2x2}

= {(a c) : [(a+b) + (c+d) (a+b) + (c+d)] = [0 0] }
{(b d) [a+b) + (c+d) (a+b) + (c+d)] [0 0] }
Isn't this the same as saying all of the 2 x 2 matrices whose entries add to 0?
mmcgirr4 said:
then

a = -b -c -d
b = -a-c-d
c = -a-b-d
d = -a-b-c
There's a more systematic way to do this.

a = -b -c -d
b = b (obviously)
c = ... c (ditto)
d = ... d (ditto)

If you stare at the right side above, you should be able to see three vectors, many of whose entries are zero.
mmcgirr4 said:
therefor the Ker(T) = {(a c) : a, b, c, d ε R^2x2}
{(b d) }
 

Related to Finding a basis for the Kernel of T

1. What is the Kernel of T?

The Kernel of T, also known as the null space, is the set of all vectors that map to the zero vector when multiplied by the linear transformation T. In other words, it is the set of all inputs that produce an output of zero.

2. Why is finding a basis for the Kernel of T important?

Finding a basis for the Kernel of T is important because it allows us to understand the structure of the null space and how it relates to the linear transformation T. It can also help us solve systems of linear equations and find solutions to certain problems.

3. How do you find a basis for the Kernel of T?

To find a basis for the Kernel of T, we need to solve the homogeneous system of linear equations represented by the linear transformation T. This can be done by setting up a matrix and using row reduction techniques to find the pivot variables and free variables. The free variables will correspond to the basis vectors for the Kernel of T.

4. Can there be more than one basis for the Kernel of T?

Yes, there can be more than one basis for the Kernel of T. This is because the Kernel of T is a subspace and any linearly independent set of vectors in that subspace can serve as a basis. However, all bases for the Kernel of T will have the same number of vectors, known as the dimension of the null space.

5. How does the dimension of the Kernel of T relate to the rank of the linear transformation T?

The dimension of the Kernel of T and the rank of T are related by the rank-nullity theorem, which states that the sum of the dimensions of the Kernel of T and the image (or range) of T is equal to the dimension of the domain of T. In other words, the dimension of the Kernel of T is equal to the dimension of the domain of T minus the rank of T.

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