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

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The discussion centers on determining the dimension of the image of a linear transformation f from R^4 to R^4, defined using specific vectors. The matrix representation of f varies depending on the chosen basis, leading to confusion about whether the dimension of the image is basis-dependent. It is clarified that the rank of a linear transformation, which indicates the dimension of the image, does not depend on the basis used; thus, any discrepancies in matrix representations signal an error in calculations. The participants confirm that the initial matrix representation was incorrect, particularly in the third column. Ultimately, the rank remains consistent across different bases, reinforcing the principle that the dimension of the image is invariant under basis changes.
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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...
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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