How do I calculate the Basis for Im(T)?

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In summary, to calculate the basis for the image of a transformation, you need to find all linearly independent vectors in the image. This can be done by finding a matrix that represents the transformation and using it to determine the image. In the given example, the image is two-dimensional and a basis can be represented by the set of vectors {(1, 0, 0), (0, 1, 0)}. However, it is important to remember that a basis is a set of vectors, not a matrix, even though a matrix may be used in the calculation.
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Badger33
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How do I calculate the Basis for Im(T)? I am having troubles finding an example that will best fir here. I know that the I=diagonal matrix with all of all of the i=j entries being 1. Beyond that I am rather confused and don't know where I need to start.
 
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Badger33 said:
How do I calculate the Basis for Im(T)? I am having troubles finding an example that will best fir here. I know that the I=diagonal matrix with all of all of the i=j entries being 1. Beyond that I am rather confused and don't know where I need to start.

This is a confusing question. If you are simply dealing with a situation where T is a matrix, the image is the column space. In other words, it is all vectors that are linearly independent in the columns.

In general, however, the image of a transformation is defined to be the set [tex] \left \{ v\in W |T(x)=v, x\in V \right \} [/tex]where V and W are vector spaces over a field. You need to be more precise as to what exactly you want.
 
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So to calculate the basis for the Im(T). I would gather all the Linear Independent parts and put them into a basis?
if I got:
[1] [0]
[0] [1]
[0], [0]

then my basis would be:
[1 0]
[0 1]
[0 0]

Or am I way off on this?
 
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From a more fundamental viewpoint, if
[tex]T= \begin{bmatrix}1 & 0 \\ 0 & 1 \\ 0 & 0\end{bmatrix}[/tex]
Then T maps [itex]R^2[/itex] to [itex]R^3[/itex]. (x, y, z) will be in the image of T if and only if there exist (a, b) such that
[tex]\begin{bmatrix}1 & 0 \\ 0 & 1 \\ 0 & 0\end{bmatrix}\begin{bmatrix}a \\ b\end{bmatrix}= \begin{bmatrix}a \\ b \\ 0\end{bmatrix}= \begin{bmatrix}x \\ y \\ z\end{bmatrix}[/tex]
which gives the equations a= x, b= y, 0= z. Since a and b could be any numbers so can x and y- but z must be 0. That is, any vector in the image of T must be of the form (x, y, 0)= (x, 0, 0)+ (0, y, 0)= x(1, 0, 0)+ y(0, 1, 0). Yes, the image is two-dimensional and a basis is the set of vectors {(1, 0, 0), (0, 1, 0)}, the columns of the matrix you show. However, it is important to remember that a basis is a set of vectors, not a matrix. Even though you used a matrix to calculate it, you should show the basis as a set of vectors, not as a matrix.
 

FAQ: How do I calculate the Basis for Im(T)?

What is the Basis for Im(T) and why is it important?

The Basis for Im(T) is the set of all possible outputs of a linear transformation T. It is important because it helps us understand the range of values that T can produce, which is crucial in many scientific and mathematical applications.

How do I calculate the Basis for Im(T) using a matrix?

To calculate the Basis for Im(T) using a matrix, first find the reduced row echelon form of the matrix. Then, the pivot columns in the reduced matrix form the Basis for Im(T).

Can I calculate the Basis for Im(T) without using a matrix?

Yes, it is possible to calculate the Basis for Im(T) without using a matrix. This can be done by finding the linearly independent vectors that span the range of the linear transformation T.

What is the difference between the Basis for Im(T) and the Basis for Ker(T)?

The Basis for Im(T) is the set of all possible outputs of T, while the Basis for Ker(T) is the set of all inputs that result in the zero vector when transformed by T. In other words, the Basis for Im(T) represents the range of T, while the Basis for Ker(T) represents the null space of T.

How can I use the Basis for Im(T) in real-world applications?

The Basis for Im(T) can be used in a variety of applications such as data compression, image processing, and signal processing. It helps in understanding the possible range of values that a transformation can produce and can be used to optimize and enhance various processes and systems.

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