Basis of image of linear transformation

In summary: Since <1, -13, 0> and <0, 5, 1> are independent, they form a basis for the image of A.In summary, to find a basis of the image im(LA) of the linear transformation LA: R^5 \rightarrowR^3, x\mapstoAx, where A =1 -2 2 3 -1-3 6 -1 1 -72 -4 5 8 -4, we can set up an augmented matrix of A with variables y1, y2, and y3 on the end. By performing row reduction, we obtain the row reduced
  • #1
karnten07
213
0

Homework Statement


Find a basis of the image im(LA) of the linear transformation

LA: R^5 [tex]\rightarrow[/tex]R^3, x[tex]\mapsto[/tex]Ax

where

A =
1 -2 2 3 -1
-3 6 -1 1 -7
2 -4 5 8 -4

and hence determine the dimension of im(LA)

The Attempt at a Solution


Using the equation LA= Ax,

can i set up an augmented matrix of A and say, y1, y2 and y3 on the end? Then perform row reduction to get it into row reduced echelon form. Is that the right way to go about it? I will perform the calculation and update with it but any direction would be greatly appreciated.
 
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  • #2
A has row reduced form of:

1 -2 0 -1 3 y1-2y3
0 0 1 2 -2 y3-2y1
0 0 0 0 0 y2-3y1-5y3

I have the solutions to the basis of the null space for A, how is this case different?
 
  • #3
I rearranged the equations to get:

y3=2x1-4x2+5x3+4x5
y1=3x1-6x2+10x3-x4+7x5

Does this even have any use, I am just getting lost with this thing. Please help, urgent.
 
  • #4
karnten07 said:
A has row reduced form of:

1 -2 0 -1 3 y1-2y3
0 0 1 2 -2 y3-2y1
0 0 0 0 0 y2-3y1-5y3

I have the solutions to the basis of the null space for A, how is this case different?
That is not what I get! You had to divide by 5 to get that "1" in the second row didn't you? In the last column, I get, for the second row, (y3- 2y1)/5 and, for the third row (5y3- 13y1-y2)/5. In order that there exist x such that Ax= y you must have 5y3- 13y1- y2= 0. That is the same as y2= 5y3- 13y1 so if you take y1= 1, y3= 0, y2 must be -13. <1, -13, 0> is in the image. If you take y1= 0, y3= 1, y2 must be 5. <0, 5, 1> is in the image.
 

1. What is the basis of an image of linear transformation?

The basis of an image of linear transformation is a set of vectors that form the span of the image of the original vector space. In other words, it is the set of all possible outcomes of the linear transformation.

2. How is the basis of an image of linear transformation determined?

The basis of an image of linear transformation can be determined by applying the linear transformation to a set of linearly independent vectors in the original vector space. The resulting vectors will form the basis of the image.

3. What is the significance of the basis of an image of linear transformation?

The basis of an image of linear transformation helps to determine the dimension of the image of the original vector space, and can also provide information about the properties and behavior of the linear transformation.

4. Can the basis of an image of linear transformation change?

Yes, the basis of an image of linear transformation can change if the linear transformation is applied to a different set of vectors in the original vector space. However, the dimension of the image will remain the same.

5. How does the basis of an image of linear transformation relate to the range of the linear transformation?

The basis of an image of linear transformation is directly related to the range of the linear transformation, as the range is the span of the basis vectors. The basis provides a basis for the range of the linear transformation.

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