Kernel and image of linear transformation

In summary, the basis for Ker T is (1,-1,0), and the basis for Im T is (1,1,0), (0,0,1). For the second example, the basis for Ker T is [(1,-1,0), (0,0,1)], and the basis for Im T is [(1,0,0), (0,1,0), (1,1,0), (0,0,1)].
  • #1
stunner5000pt
1,461
2
Find a basis for Ker T and a basis for I am T

a) T: P_{2} -> R^2 \ T(a+bx+cx^2) = (a,b)

for Ker T , both a and b must be zero, but c can be anything
so the basis is x^2

for hte image we have to find the find v in P2 st T(v) = (a,b) \in P^2
the c can be anything, right?
cant our basis be (1,0) or (0,1) ??
But wshould hte dimensions of the kernel and image add up to the dimension of the preimage?

Latex is acting funny...
 
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  • #2
for Ker T , both a and b must be zero, but c can be anything
so the basis is (0,0,1)
I think you have the right idea, but there's a problem: (0, 0, 1) isn't a polynomial! It's not an element of P_{2}!


for hte image we have to find the find v in P2 st T(v) = (a,b) \in P^2
the c can be anything, right?
cant our basis be (1,1,0) or (1,0,1) or (1,0,0) ??
I'm not really sure at all what you're doing here.

But there is one thing that's clearly wrong: T is a map from P_{2} to R². Therefore, the image of T is a subset of R². So, a basis of it must consist of elements of R².
 
  • #3
Hurkyl said:
I think you have the right idea, but there's a problem: (0, 0, 1) isn't a polynomial! It's not an element of P_{2}!

you're right i corrected it, it should be x^2, yes?


Hurkyl said:
I'm not really sure at all what you're doing here.

But there is one thing that's clearly wrong: T is a map from P_{2} to R². Therefore, the image of T is a subset of R². So, a basis of it must consist of elements of R².

also corrected should be (1,0), (0,1)
 
  • #4
a couple of more questions

b) T:R3->R3, T(x,y,z) = (x+y,x+y,0)

basis for ker T-> (1,-1,0)

basis for the imT = (1,1,0),(0,0,1)

c)
[tex] T: M_{22} \rightarrow M_{22 [/tex]
[tex] T \left[\begin{array}{cc} a&b \\ c&d \end{array}\right] = \left[\begin{array}{cc} a+b&b+c \\ c+d&d +a\end{array}\right][/tex]
for the ker T: a =-b and b = -c, c= -d, a = -d
basis [tex] \left[\begin{array}{cc} 1&-1 \\ 0&0 \end{array}\right] [/tex]
[tex] \left[\begin{array}{cc} 0&0 \\ 1&-1 \end{array}\right] [/tex]

im T basis includes
[tex] \left[\begin{array}{cc} 1&0 \\ 0&1 \end{array}\right] [/tex]
[tex] \left[\begin{array}{cc} 0&1 \\ 1&0 \end{array}\right] [/tex]
[tex] \left[\begin{array}{cc} 1&1 \\ 0&0 \end{array}\right] [/tex]
[tex] \left[\begin{array}{cc} 0&0 \\ 1&1 \end{array}\right] [/tex]
 
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1. What is the difference between the kernel and image of a linear transformation?

The kernel of a linear transformation is the set of all inputs (vectors) that map to the zero vector in the output space. The image, on the other hand, is the set of all possible outputs that can be obtained by applying the transformation to all inputs in the input space. In other words, the kernel represents what is "lost" or "ignored" during the transformation, while the image represents what is actually "seen" or "covered" by the transformation.

2. How do you determine the dimension of the kernel and image?

The dimension of the kernel can be found by counting the number of free variables in the system of equations that represent the linear transformation. The dimension of the image can be found by counting the number of linearly independent columns in the matrix representation of the linear transformation.

3. Can the kernel and image of a linear transformation be equal?

Yes, it is possible for the kernel and image to be equal. This happens when the linear transformation is onto, meaning that every vector in the output space is covered by at least one input vector. In this case, the dimension of the kernel is 0, meaning there are no free variables in the system of equations representing the transformation.

4. How do the kernel and image relate to the null space and column space of a matrix?

The kernel of a linear transformation is equivalent to the null space of the corresponding matrix, while the image is equivalent to the column space. This means that the dimension and properties of the kernel and image can be found by performing row reduction on the matrix and examining the resulting reduced row-echelon form.

5. Can the kernel and image of a linear transformation change?

Yes, the kernel and image of a linear transformation can change depending on the choice of basis for the input and output spaces. However, the dimension and properties of the kernel and image will remain the same regardless of the choice of basis.

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