Dimension of a Linear Transformation Matrix

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Discussion Overview

The discussion revolves around the dimensionality of a matrix representing a linear transformation from the space of 2x2 matrices, denoted as ##M_{2}(R)##, to itself. Participants explore the implications of this transformation on matrix dimensions and the representation of linear transformations in finite-dimensional vector spaces.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant questions whether the matrix of the linear transformation should have the same dimension as the vector space, noting that they found a 4x4 matrix representation.
  • Another participant confirms that the matrix should indeed be 4x4, as the transformation maps ##M_{2}(R)## to itself, and the dimension of ##M_{2}(R)## is 4.
  • A third participant elaborates on the general rule that a linear transformation from a vector space of dimension ##n## to another of dimension ##m## is represented by an ##m \times n## matrix, affirming that both spaces here are of dimension 4.
  • A participant expresses confusion about how to apply a 4x4 matrix to matrices in the ##M_{2}(R)## space.
  • Another participant explains that if using the transformation as a linear operation on ##\mathbb{R}^2##, one should consider the 2x2 matrix directly, while also discussing the need for an ordered basis when treating ##M_{2}(R)## as a vector space.
  • They provide an example of how to express a 2x2 matrix in terms of a basis and how a linear transformation can be represented in this context.

Areas of Agreement / Disagreement

Participants generally agree that the matrix representation of the linear transformation should be 4x4 due to the dimensionality of the involved spaces. However, there is some uncertainty regarding the application of this matrix to matrices in ##M_{2}(R)## and how to interpret the transformation in different contexts.

Contextual Notes

Participants mention the importance of choosing an ordered basis for ##M_{2}(R)## when considering it as a vector space, which may affect how the transformation is applied and understood.

patric44
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does the matrix of the linear transformation should have the same dimension of the vector space?
hi guys
I was trying to find the matrix of the following linear transformation with respect to the standard basis, which is defined as
##\phi\;M_{2}(R) \;to\;M_{2}(R)\;; \phi(A)=\mu_{2*2}*A_{2*2}## ,
where ##\mu = (1 -1;-2 2)##
and i found the matrix that corresponds to this linear transformation this 4*4 matrix , given by
$$\phi =(1\;0\;-2\;0;0\;1\;0-2;-1\;0\;2\;0;0 -1\;0\;2) $$
is there is some wrong here or the matrix should be 4*4
 
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patric44 said:
Summary:: does the matrix of the linear transformation should have the same dimension of the vector space?

hi guys
I was trying to find the matrix of the following linear transformation with respect to the standard basis, which is defined as
##\phi\;M_{2}(R) \;to\;M_{2}(R)\;; \phi(A)=\mu_{2*2}*A_{2*2}## ,
where ##\mu = (1 -1;-2 2)##
and i found the matrix that corresponds to this linear transformation this 4*4 matrix , given by
$$\phi =(1\;0\;-2\;0;0\;1\;0-2;-1\;0\;2\;0;0 -1\;0\;2) $$
is there is some wrong here or the matrix should be 4*4
The matrix should be 4 x 4, since your transformation is a map from ##M_2## to itself. ##M_2##, the space of 2 x 2 matrices, is of dimension 4, and any basis for this space will need to have 4 elements.
 
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A linear transformation ##\Phi: V\longrightarrow W## of finite-dimensional vector spaces, say ##\dim V = n## and ##\dim W = m## has a matrix representation as an ##m \times n## matrix, ##n## columns and ##m## rows.

We have here ##V=W=\mathbb{R}^2,## the vector space of linear functions ##\varphi : \mathbb{R}^2 \longrightarrow \mathbb{R}^2##. Since ##\dim \mathbb{R}^2 = 2##, the dimension of ##\mathbb{M}_2(\mathbb{R})## is ##2\cdot 2= 4.##

In case ##V=W=\mathbb{M}_2(\mathbb{R})## we get ##\dim V = \dim W=\dim \mathbb{M}_2(\mathbb{R})=4,## we have ##\dim \mathbb{M}_4(\mathbb{R})=4\cdot 4=16## and ##\Phi## is represented by a ##4 \times 4## matrix.
 
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thanks guys i think i got it now, but i still have a question : how iam going to act with a 4*4 matrix on matrices that belong to ##M_{2}(R)## space
 
If you use them as a linear transformation of ##\mathbb{R}^2## then you simply consider the ##2\times 2## matrix.

If you want to deal with all those linear transformations and deal with ##\mathbb{M}_2(\mathbb{R})## as vector space instead, then you have to choose some ordered basis to make those matrices a linear vector. E.g. if ##e_{ij}## is the matrix with ##1## in the ## i ##-th row and ##j##-th column, and zeros otherwise, then ##(e_{11},e_{12},e_{21},e_{22})## would be such a bases.

A ##2 \times 2## matrix would be
##\begin{bmatrix}
2&3\\4&5
\end{bmatrix}=2e_{11}+3e_{12}+4e_{21}+5e_{22}=(2,3,4,5)##

A linear transformation between those, e.g. ##M_{\Phi}=\begin{bmatrix}
2&3&0&1\\0&0&0&5\\0&0&1&4\\0&0&0&0
\end{bmatrix}##
would map
\begin{align*}
e_{11}&\longmapsto 2e_{11}+3e_{12}+1e_{22}\\
e_{12}&\longmapsto 5e_{22}\\
e_{21}&\longmapsto 1e_{21}+4e_{22}\\
e_{22}&\longmapsto 0
\end{align*}Edit: This interpretation corresponds to ##\vec{v} \longmapsto \vec{v}\cdot M_{\Phi}##. If we choose matrix multiplication from the left, i.e. ##\vec{v} \longmapsto M_{\Phi}\cdot \vec{v}## then
\begin{align*}
e_{11}&\longmapsto 2e_{11}\\
e_{12}&\longmapsto 3e_{11}\\
e_{21}&\longmapsto 1e_{21}\\
e_{22}&\longmapsto 1e_{11}+5e_{12}+4e_{21}
\end{align*}
 
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