Nullity, rank, image and kernel answer check

In summary, the linear mapping T : R2->R3 can be represented by the matrix A = {(1,-1),(-2,2),(0,0)} with respect to the standard bases of R2 and R3. The bases for the image of T are (1,-2,0) and the bases for the null-space are (1,1). The rank of T is 1 and the nullity is 1.
  • #1
franky2727
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My question is let the linear mapping T : R2->R3 be given by T(x,y)=(x-y,2y-2x,0)

write down bases for its image and null-space and determine its rank and nullity.
Find the matrix A that represents T with respect to the standard bases of R2 and R3

now i think i know how to do this but I'm revising and have no answers and would hate to be revising the wrong things, so can i please have a moment of someones time to confirm this is right, thanks

firstly A= {(1,-1),(-2,2),(0,0)}

kernal is when y=x so bases for a kernal is a(1,1) and n(t)=1

image is b(1,-2,0) r(t)=1

so r(t)+n(t)=2 as expected
 
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  • #2
Looks ok to me.
 

FAQ: Nullity, rank, image and kernel answer check

What is nullity in linear algebra?

Nullity refers to the dimension of the null space, also known as the kernel, of a linear transformation. It represents the number of linearly independent vectors that map to the zero vector.

How is rank calculated in linear algebra?

The rank of a matrix is equal to the number of linearly independent rows or columns. It can also be calculated by finding the number of pivot positions in the reduced row echelon form of the matrix.

What is the image of a linear transformation?

The image of a linear transformation is the set of all possible outputs that can be obtained by applying the transformation to the input vectors. It is also known as the range of the transformation.

How do you check if a vector is in the kernel of a linear transformation?

To check if a vector is in the kernel of a linear transformation, you can multiply the vector by the transformation matrix and see if the resulting vector is the zero vector. If it is, then the vector is in the kernel.

Can the nullity of a matrix be greater than its rank?

Yes, the nullity of a matrix can be greater than its rank. This happens when the number of linearly independent rows or columns is less than the number of rows or columns, respectively. In other words, when there are more free variables than pivot variables.

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