Linear transformation and its matrix

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Discussion Overview

The discussion revolves around finding the matrix representation of a linear transformation \(\xi:\mathbb{R}^2\mapsto\mathbb{R}^2\) given specific transformations of vectors and a new basis \(\mathcal{B}\). The focus is on the theoretical aspects of linear transformations and their matrix representations in different bases.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Homework-related

Main Points Raised

  • One participant presents a linear transformation with specific outputs for given input vectors and asks how to find the corresponding matrix in a new basis.
  • Another participant suggests that the components of a linear transformation can be determined using the relationship between the basis vectors and the transformation's outputs.
  • A later reply provides a detailed method for expressing the new basis vectors in terms of the original vectors, applying the linear transformation, and then expressing the results back in terms of the new basis to find the matrix representation.
  • Participants engage in a step-by-step approach to derive the matrix columns based on the transformation outputs and the new basis vectors.

Areas of Agreement / Disagreement

Participants generally agree on the method of finding the matrix representation of the linear transformation, but there is no explicit consensus on the final matrix or any potential alternative methods.

Contextual Notes

The discussion includes various assumptions about the linear transformation and the bases involved, which may not be fully articulated. Some mathematical steps are presented without resolution, particularly regarding the final matrix representation.

lukaszh
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Hello everybody,
I have a problem. There is a linear trasformation [tex]\xi:\mathbb{R}^2\mapsto\mathbb{R}^2[/tex] and:
[tex]\xi\begin{pmatrix}3\\1\end{pmatrix}=\begin{pmatrix}2\\-4\end{pmatrix}[/tex]
[tex]\xi\begin{pmatrix}1\\1\end{pmatrix}=\begin{pmatrix}0\\2\end{pmatrix}[/tex]
How to find a matrix for this linear transformation for basis [tex]\mathcal{B}=\left\{\begin{pmatrix}1\\2\end{pmatrix},\begin{pmatrix}2\\1\end{pmatrix}\right\}[/tex]

Thank you so much :-)
 
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There are a number of ways to do this. What have you tried so far? In general if you have a finite dimensional vector space V with a basis [tex]\{ e_{1},e_{2},...,e_{n}\}[/tex] the components of a linear transformation [tex]T:V\rightarrow V[/tex] are uniquely determined by [tex]Te_{i}=T^{j}_{i}e_{j}[/tex] where the sum over j from 1 to n is implied. Furthermore, if you have a second basis [tex]\{f_1,f_2,...,f_n\}[/tex] then the new and old basis vectors are related through [tex]e_{i}=A^{j}_{i}f_{j}[/tex].
 
lukaszh said:
Hello everybody,
I have a problem. There is a linear trasformation [tex]\xi:\mathbb{R}^2\mapsto\mathbb{R}^2[/tex] and:
[tex]\xi\begin{pmatrix}3\\1\end{pmatrix}=\begin{pmatrix}2\\-4\end{pmatrix}[/tex]
[tex]\xi\begin{pmatrix}1\\1\end{pmatrix}=\begin{pmatrix}0\\2\end{pmatrix}[/tex]
How to find a matrix for this linear transformation for basis [tex]\mathcal{B}=\left\{\begin{pmatrix}1\\2\end{pmatrix},\begin{pmatrix}2\\1\end{pmatrix}\right\}[/tex]

Thank you so much :-)
Write each of the vectors in B in terms of the first basis, <3, 1>, and <1, 1>, so that you can apply the linear transformation to them. For example, <1, 2>= a<3, 1>+ b<1, 1>= <3a+ b, a+ b>. 3a+ b= 1, a+ b= 2. Subtracting the second equation from the first, 2a= -1 so a= -1/2. Then b= 5/2: <1, 2>= (-1/2)<3,1>+ (5/2)<1, 1>. Applying the [itex]\xi[/itex] to that, [itex]\xi[/itex]<1, 2>= (-1/2)[itex]\xi[/itex]<3,1>+ (5/2)[itex]\xi[/itex]<1,1>= (-1/2)<2, -4>+ (5/2)<0, 2>= <-1, 1>. Now write that in terms of <1, 2> and <2, 1>: <-1, 1>= a<1, 2>+ b<2, 1>= <a+ 2b, 2a+ b>. a+ 2b= -1 and 2a+ b= 1. Subtracting twice the first equation from the second, -3b= 3 so b= -1. Then a= 1. [itex]\xi[/itex]<1, 2>= (1)<1, 2>+ (-1)<2, 1> so the first column of the matrix representing [itex]\xi[/itex] in this basis is [1 -1].

Do the same with <2, 1> to find the second column.
 
Thank you both very much :-)
 

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