Dimension of the image of a linear transformation dependent on basis?

In summary, the conversation discusses determining the dimension of the image of a linear transformation, represented by a given matrix, under multiple bases. It is found that the rank of the matrix is equal to the dimension of the image for the natural basis, but not for the given basis. After realizing an error in the given matrix, it is concluded that the rank and dimension of the image do not depend on the basis. The conversation also touches on the concept of the kernel, which also does not depend on the basis.
  • #1
dane502
21
0
First of all I would like to wish a happy new year to all of you, who have helped us understand college math and physics. I really appreciate it.

Homework Statement



Determine the dimension of the image of a linear transformations [tex]f^{\circ n}[/tex], where [tex]n\in\mathbb{N}[/tex] and [tex]f:\mathbb{R}^4\to\mathbb{R}^4[/tex] where
[tex]f(\underline{x})=(\underline{x}\cdot\underline{a_1}) \underline{a_2} +
(\underline{x}\cdot\underline{a_2}) \underline{a_3} +
(\underline{x}\cdot\underline{a_3}) \underline{a_4}[/tex]

and
[tex]
\underline{a_1} =
\begin{pmatrix}
1 \\
1 \\
0 \\
0
\end{pmatrix}
,
\underline{a_2} =
\begin{pmatrix}
0 \\
0 \\
2 \\
0
\end{pmatrix}
,
\underline{a_3} =
\begin{pmatrix}
0 \\
0 \\
0 \\
1
\end{pmatrix}
,
\underline{a_4} =
\begin{pmatrix}
1 \\
-1 \\
0 \\
0
\end{pmatrix}
[/tex]

Homework Equations



The matrix representation of [tex]f[/tex] with regards to the natural basis is

[tex]
\underline{\underline{C}}=
\begin{pmatrix}
0&0&0&1\\
0&0&0&-1\\
2&2&0&0\\
0&0&2&0
\end{pmatrix}
[/tex]

but with regards to the basis [tex]\mathcal{A} = (\underline{a_1},\ldots,\underline{a_4})[/tex] the matrix representation of [tex]f:\mathcal{A}\to\mathcal{A}[/tex] is

[tex]
\underline{\underline{A}} =
\begin{pmatrix}
0 & 0 & 1 & 0 \\
2 & 0 & 0 & 0 \\
0 & 4 & 0 & 0 \\
0 & 0 & 1 & 0 \\
\end{pmatrix}
[/tex]

The Attempt at a Solution



But the rank of a matrix equals the dimension of the image of the corresponding transformation. However, [tex]\text{Rk}(\underline{\underline{A}}^n) = \text{Rk}(\underline{\underline{C}}^n)[/tex] only for [tex]n=1[/tex], which puzzles me for two reasons. Firstly, because I cannot solve problem. Secondly, because it implies that the dimension of the image of a linear transformation depends on the basis, which contradicts my "visualization" of changes of basis.
 
Physics news on Phys.org
  • #2
I think the matrix A is incorrect. Specifically, I don't think that the third column is correct...
 
  • #3
micromass said:
I think the matrix A is incorrect. Specifically, I don't think that the third column is correct...

Thanks for your fast response - Your right. The entry in column 3 row 1 should be a 0 and not a 1.
Which makes [tex]
\text{Rk}(\underline{\underline{A}}^n) = \text{Rk}(\underline{\underline{C}}^n)
[/tex] for all [tex]
n\in\mathbb{N}
[/tex].

Would someone care to comment on whether or not the dimension of the image of a linear transformation (or rank of its matrix representation) depends on the basis being used in general?
 
  • #4
Well, the rank of a linear transformation does not depend on the basis. So in this case, you would know immediately that you made a mistake.
Also the kernel of a transformation does not depend on the basis...
 

Related to Dimension of the image of a linear transformation dependent on basis?

1. What is the definition of the image of a linear transformation?

The image of a linear transformation is the set of all possible outputs or values that the transformation can produce when applied to a given input. It is also known as the range of the transformation.

2. How does the basis affect the dimension of the image of a linear transformation?

The basis of a linear transformation is a set of vectors that span the entire input space. The dimension of the image of a linear transformation is dependent on the number of vectors in the basis, as it determines the number of independent directions in which the transformation can map the input space.

3. Can the dimension of the image of a linear transformation be larger than the dimension of the input space?

No, the dimension of the image of a linear transformation cannot be larger than the dimension of the input space. This is because the transformation can only map the input space to a subspace of the same dimension or lower.

4. How can the basis be chosen to maximize the dimension of the image of a linear transformation?

The basis can be chosen by finding a set of linearly independent vectors that span the entire input space. These vectors can be chosen either through trial and error or by using techniques such as Gram-Schmidt orthogonalization.

5. Is the dimension of the image of a linear transformation always equal to the dimension of the input space?

No, the dimension of the image of a linear transformation can be less than the dimension of the input space in certain cases. This can happen when the transformation maps some of the input space's dimensions to the same output value, resulting in a smaller subspace of the same dimension as the input space.

Similar threads

  • Calculus and Beyond Homework Help
Replies
6
Views
370
  • Calculus and Beyond Homework Help
Replies
2
Views
600
  • Calculus and Beyond Homework Help
Replies
6
Views
768
  • Calculus and Beyond Homework Help
Replies
2
Views
1K
  • Calculus and Beyond Homework Help
Replies
4
Views
1K
  • Calculus and Beyond Homework Help
Replies
3
Views
938
  • Calculus and Beyond Homework Help
Replies
3
Views
876
  • Calculus and Beyond Homework Help
Replies
4
Views
689
  • Calculus and Beyond Homework Help
Replies
8
Views
2K
  • Calculus and Beyond Homework Help
Replies
12
Views
1K
Back
Top