Linear Transformations,Find basis of kernel and range

In summary, to find the basis of the kernel and range of T:R4 to R3, you will need to solve systems of equations for both the kernel and image. For the kernel, you will need to solve a system of equations with 3 variables and for the image, you will need to solve a system with parameters. To find the basis of the image, you will first need to find a basis of R4 and then take the images of those basis vectors. If the resulting set is linearly independent, it will be a basis for the image. Otherwise, remove some vectors until it is linearly independent. An equation that may help in this process is dim(ker(f)) + dim(Im(f)) = dim
  • #1
nehap.2491
4
0
suppose that vectors in R3 are denoted by 1*3 matrices, and define T:R4 to R3 by T9x,y,z,t)=(x-y+z+t,2x-2y+3z+4t,3x-3y+4z+5t).Find basis of kernel and range.
 
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  • #2
First you'll need to calculate what the kernel and image is of T.

For the kernel, you'll need to solve the following system

[tex] \left\{ \begin{array}{c}
x-y+z+t = 0\\
2x-2y+3z+4t = 0\\
3x-3y+4z+5t = 0
\end{array}\right. [/tex]

For the image, you'll have to solve

[tex] \left\{ \begin{array}{c}
x-y+z+t = \alpha\\
2x-2y+3z+4t = \beta\\
3x-3y+4z+5t = \gamma
\end{array}\right. [/tex]

where alpha, beta, gamma are parameters. You'll then have to see for which alpha, beta, gamma this system has a solution.
 
  • #3
Ow, for the image you won't need to do all that stuff, I'm sorry.

You'll first have to find a basis of R4, call this {e1,e2,e3,e4}. Then {T(e1),T(e2),T(e3),T(e4)} is a set which spans the image. If this set is linear independant, then it's a basis. If not, then remove some vectors until it is linear independant...
 
  • #4
If you are open to it, the equation below (in case f : V -> W) might even help:

dim(ker(f)) + dim(Im(f)) = dim(V)
 
  • #5
micromass said:
Ow, for the image you won't need to do all that stuff, I'm sorry.

You'll first have to find a basis of R4, call this {e1,e2,e3,e4}. Then {T(e1),T(e2),T(e3),T(e4)} is a set which spans the image. If this set is linear independant, then it's a basis. If not, then remove some vectors until it is linear independant...
Thank you!
 
  • #6
Outlined said:
If you are open to it, the equation below (in case f : V -> W) might even help:

dim(ker(f)) + dim(Im(f)) = dim(V)
Thank you!
 

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another in a way that preserves the linear structure of the original space.

2. How do you find the basis of a kernel?

To find the basis of a kernel, you can set up a system of equations using the matrix representation of the linear transformation and solve for the variables. The basis of the kernel will be the set of vectors that satisfy the system of equations when plugged into the transformation.

3. What does it mean to find the range of a linear transformation?

The range of a linear transformation is the set of all possible output vectors that can be obtained by applying the transformation to all possible input vectors. In other words, it is the span of the columns of the matrix representation of the transformation.

4. How can you determine if a linear transformation is one-to-one?

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 can produce the same output vector.

5. Can a linear transformation have a non-trivial kernel and range?

Yes, a linear transformation can have a non-trivial kernel and range. This means that there are input vectors that produce a zero output vector and there are output vectors that cannot be obtained by applying the transformation to any input vector, respectively.

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