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

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Homework Help Overview

The discussion revolves around determining the dimension of the image of a linear transformation defined on \(\mathbb{R}^4\). The transformation is expressed in terms of specific vectors and their interactions, with participants exploring the implications of different matrix representations based on varying bases.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants examine the relationship between the rank of the transformation's matrix representation and the basis used. There is a focus on whether the dimension of the image can vary with different bases, leading to questions about the correctness of the matrix representation.

Discussion Status

The conversation includes attempts to verify the correctness of the matrix representation and its implications for the rank of the transformation. Some participants express confusion regarding the dependence of the dimension of the image on the basis, while others assert that the rank should remain invariant across bases.

Contextual Notes

There are indications of potential errors in the matrix representation, which participants are actively discussing. The original poster expresses uncertainty about the implications of these errors on their understanding of linear transformations and basis changes.

dane502
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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 f^{\circ n}, where n\in\mathbb{N} and f:\mathbb{R}^4\to\mathbb{R}^4 where
f(\underline{x})=(\underline{x}\cdot\underline{a_1}) \underline{a_2} +<br /> (\underline{x}\cdot\underline{a_2}) \underline{a_3} +<br /> (\underline{x}\cdot\underline{a_3}) \underline{a_4}

and
<br /> \underline{a_1} =<br /> \begin{pmatrix}<br /> 1 \\<br /> 1 \\<br /> 0 \\<br /> 0 <br /> \end{pmatrix}<br /> ,<br /> \underline{a_2} =<br /> \begin{pmatrix}<br /> 0 \\<br /> 0 \\<br /> 2 \\<br /> 0<br /> \end{pmatrix}<br /> ,<br /> \underline{a_3} =<br /> \begin{pmatrix}<br /> 0 \\<br /> 0 \\<br /> 0 \\<br /> 1 <br /> \end{pmatrix}<br /> ,<br /> \underline{a_4} =<br /> \begin{pmatrix}<br /> 1 \\<br /> -1 \\<br /> 0 \\<br /> 0<br /> \end{pmatrix}<br />

Homework Equations



The matrix representation of f with regards to the natural basis is

<br /> \underline{\underline{C}}=<br /> \begin{pmatrix}<br /> 0&amp;0&amp;0&amp;1\\<br /> 0&amp;0&amp;0&amp;-1\\<br /> 2&amp;2&amp;0&amp;0\\<br /> 0&amp;0&amp;2&amp;0<br /> \end{pmatrix}<br />

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

<br /> \underline{\underline{A}} =<br /> \begin{pmatrix}<br /> 0 &amp; 0 &amp; 1 &amp; 0 \\<br /> 2 &amp; 0 &amp; 0 &amp; 0 \\<br /> 0 &amp; 4 &amp; 0 &amp; 0 \\<br /> 0 &amp; 0 &amp; 1 &amp; 0 \\<br /> \end{pmatrix}<br />

The Attempt at a Solution



But the rank of a matrix equals the dimension of the image of the corresponding transformation. However, \text{Rk}(\underline{\underline{A}}^n) = \text{Rk}(\underline{\underline{C}}^n) only for n=1, 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.
 
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I think the matrix A is incorrect. Specifically, I don't think that the third column is correct...
 
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 <br /> \text{Rk}(\underline{\underline{A}}^n) = \text{Rk}(\underline{\underline{C}}^n)<br /> for all <br /> n\in\mathbb{N}<br />.

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?
 
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...
 

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