Trouble understanding linear transformations in this context

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Homework Help Overview

The discussion revolves around understanding linear transformations in the context of subspaces of ##ℝ^n##, specifically how these subspaces relate to homogeneous systems of linear equations. The original poster seeks clarity on the implications of a linear transformation defined by a basis of a subspace and its extension to the entire space.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the construction of a linear transformation ##T## and its application to a basis of a subspace. The original poster expresses uncertainty about their approach and whether they need to demonstrate that the transformation leads to the zero vector. Others suggest working through specific examples to clarify the general concept.

Discussion Status

Some participants have provided guidance on writing the matrix form of the equations and suggested concrete examples to aid understanding. There is an ongoing exploration of the relationship between different bases and the transformation matrix, but no consensus has been reached on the best approach to take.

Contextual Notes

The original poster indicates a lack of confidence in their understanding and seeks feedback on their reasoning. There is an emphasis on the need to clarify the transformation's implications and the relationship between the subspace and the homogeneous system.

Eclair_de_XII
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Homework Statement


"Show that every subspace of ##ℝ^n## is the set of solutions to a homogeneous system of linear equations. (Hint: If a subspace ##W## consists of only the zero vector or is all of ##ℝ^n##, ##W## is the set of solutions to ##IX=0## or ##0_vX=0##, respectively.

Assume ##W## is not one of these two subspaces. Let ##β=\{v_1,...,v_k\}## for ##W##. Extend this basis to ##β`=β∪\{v_{k+1},...,v_n\}## to span all of ##ℝ^n##. Let ##T: ℝ^n → ℝ^n## be the linear transformation so that ##T(v_1)=T(v_2)=...=T(v_k)=0## and ##T(v_j)=I(v_j)## for ##k+1≤j≤n##, where ##I(v)## is the identity function. Now use ##T## to obtain a matrix A so that ##W## is the set of solutions to the homogeneous system ##AX=0_v##.)"

Homework Equations


##T_\alpha=PT_βP^{-1}##

where ##P## is the transition matrix from basis ##β## to basis ##α##.

The Attempt at a Solution


##T(c_1v_1+...+c_kv_k)=T(c_1v_1)+...+T(c_kv_k)=c_1T(v_1)+...+c_kT(v_k)=0_v##
##T(c_{k+1}v_{k+1}+...+c_nv_n)=c_{k+1}T(v_{k+1})+...+c_nT(v_n)=c_{k+1}v_{k+1}+...+c_nv_n=0_v##

I honestly don't know what I'm doing here, and if anyone would like to provide feedback on what I'm doing wrong, or what I should actually be doing instead of this, that would be much appreciated. Do I have to left-multiply the ##n×n## transformation matrix by a column vector whose entries consist of the constants ##c_i##, proving that the product is the zero vector, and that it solves the homogeneous system? ##\left[T(v)\right]\left[c_i\right]=\left[0_v\right]##. Something like that, maybe? Sorry, and thanks.
 
Last edited:
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Consider the diagonal matrix \Lambda = \mathrm{diag}(\lambda_1, \dots, \lambda_n) and the matrix P = \begin{pmatrix} v_1 & v_2 & \dots & v_n\end{pmatrix} where \{v_i : i = 1, 2, \dots, n\} is a basis.

What is P\Lambda P^{-1} v_i?
 
I would suggest firstly that you write the matrix form of the equation in the first constructed basis where you have a basis of the subspace W extended to the rest of the space. Try this with say 3 dimensions and W the x-y plane. Work out the details of the concrete example and then see if you can understand the generalization.

Once you have the system of equations in the original basis you transform to the arbitrary basis with the T matrix.
 
pasmith said:
What is ##P\Lambda P^{-1} v_i##?

It looks a solution to ##\Lambda v_i## using a different basis, I think

jambaugh said:
Try this with say 3 dimensions and W the x-y plane.

Okay, so...

Let ##W⊆ℝ^2## be spanned by ##\beta=\{b_1,b_2\}##. Then let ##\beta'=\beta∪\{y\}##.

Let ##P=\left[b_1|b_2|y\right] = \begin{pmatrix}
a & d & x \\
b & e & y \\
c & f & z \end{pmatrix}##.

Then I apply the transformation matrix,

##T=\begin{pmatrix}
0 & 0 & 0 \\
0 & 0 & 0 \\
0 & 0 & 1 \\
\end{pmatrix}##

So... ##PT=\begin{pmatrix}
0 & 0 & x \\
0 & 0 & y \\
0 & 0 & z \\
\end{pmatrix}##
 

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