Understanding rectangular matrices

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Discussion Overview

The discussion revolves around the interpretation of rectangular matrices in the context of a system of equations. Participants explore the relationship between the matrix representation of the equations, the dimensionality of the vectors involved, and the implications of mapping higher-dimensional spaces to lower-dimensional ones. The scope includes conceptual clarification and mathematical reasoning.

Discussion Character

  • Conceptual clarification, Debate/contested, Mathematical reasoning

Main Points Raised

  • One participant presents a system of equations and its matrix representation, questioning the interpretation of the columns as vectors and the dimensionality involved.
  • Another participant asks for clarification on which 2D vectors are being referenced and notes the independence of the equations.
  • A participant explains that the mapping involves 6 variables in a 6-dimensional space being represented in a 2-dimensional space, suggesting that the images of the unit vectors are not mutually perpendicular.
  • There is a discussion about the nature of the vectors represented in the matrix, with some participants asserting that the images of the unit vectors do not correspond to perpendicular vectors in the 2D space.
  • One participant draws an analogy to a standard Ax=b formulation, questioning why the interpretation differs in the context of the rectangular matrix.
  • Another participant reiterates the mapping concept and emphasizes that the images of certain unit vectors are not distinct in the 2D space.
  • There is a continued exploration of the idea of perpendicularity in the context of the 2D images derived from the 6D vectors, with some participants expressing confusion over the geometric interpretation.

Areas of Agreement / Disagreement

Participants express differing views on the interpretation of the vectors and their properties, particularly regarding the concept of perpendicularity in the context of the mapping from a higher-dimensional space to a lower-dimensional one. The discussion remains unresolved with multiple competing interpretations present.

Contextual Notes

Participants reference the dimensionality of the spaces involved and the nature of the equations, but there are unresolved assumptions regarding the geometric interpretation of the vectors and their relationships.

Urmi Roy
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So if I have a system of equations:
$$x_1+x_2+x_3=1$$
and $$x_4+x_5+x_6=1$$

Then they can be put into a matrix representation
\begin{equation*}
\begin{bmatrix}
1 & 1 & 1 & 0 & 0 & 0 \\
0 & 0 & 0 & 1 & 1 & 1\\
\end{bmatrix}
\begin{bmatrix}
x_1 \\
x_2 \\
x_3 \\
x_4 \\
x_5 \\
x_6 \\
\end{bmatrix}
=
\begin{bmatrix}
1 \\
1 \\
\end{bmatrix}
\end{equation*}

So I know that columns in a matrix represent vectors. Is it true that in this matrix we therefore have 6 2D vectors?

Also it looks like there are only 2 vectors, and 3 each of them.

It's just surprising to me that there are 6 variables and only 2D vectors.

If I imagine them to be 2D vectors in x-y plane, then they are also mutually perpendicular. So eventually, the equations I mentioned above, although they look like planes, are they just really the x and y axes?

I guess I'm confused in going from the vector interpretation to the equation interpretation of the matrix.

I would appreciate help in understanding how to interpret the matrix form
 
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Does this originate in some textbook problem? Could you then please state it from the book...

It is not clear to me which 2D-vectors you are talking about. Also, your two equations are independent in the sense that they can be solved separately, and I see no reason to put them together in a system of equations.
 
It's from a linear programming question in standard form. It's a part of a worksheet we got, but I didn't want help with the whole question (so I didn't put it in the homework help forum), just understanding the meaning of the rectangular matrix. I've attached the question here for context.

By 2D vectors I mean
\begin{pmatrix}
1\\
0
\end{pmatrix}
and
\begin{pmatrix}
0\\
1
\end{pmatrix}

There are 3 each of them in the matrix.
 

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You are mapping 6 variables (so a 6 dimensional space) onto 2 numbers (so a 2 dimensional space).
The 6 2D vectors you mention are the images of the 6 unit vectors in the 6D domain space.
So ##(1,0,0,0,0,0,0)\rightarrow(1,0)## etc.
These images are clearly not perpendicular: ##(0,1,0,0,0,0,0)\rightarrow(1,0)## also, etc.

With 6 DImensions and 2 constraints (your 2 equations) you have four degrees of freedom.
6D vectors that satisfy your equations look like e.g. ##(x-1, x_2, 1-x_1-x_2,x_4,x_5,1-x_4-x_5)## so they are definitely not the axes.
 
Last edited:
BvU said:
You are mapping 6 variables (so a 6 dimensional space) onto 2 numbers (so a 2 dimensional space).
The 6 2D vectors you mention are the images of the 6 unit vectors in the 6D domain space.
So ##(1,0,0,0,0,0,0)\rightarrow(1,0)## etc.
These images are clearly not perpendicular: ##(0,1,0,0,0,0,0)\rightarrow(1,0)## also, etc.

With 6 DImensions and 2 constraints (your 2 equations) you have four degrees of freedom.
6D vectors that satisfy your equations look like e.g. ##(x-1, x_2, 1-x_1-x_2,x4,x_5,1-x_4-x_5)## so they are definitely not the axes.

Can you elaborate a bit more? In a regular Ax=b formulation, where A is, say, a 3x3 matrix, the columns are vectors in 3D space. the elements of x are 'weighting factors' which multiply the columns (vectors) and the result b is the vector sum.

So we have

\begin{equation*}
\begin{pmatrix}
1 & 2 & 3\\
5 & 7 & 8 \\
3 & 1 & 12\\
\end{pmatrix}
\begin{pmatrix}
x\\
y \\
z\\
\end{pmatrix}
=
\begin{pmatrix}
1x+2y+3z\\
5x+7y+8z \\
3x+1y+12z\\
\end{pmatrix}
=
x\begin{pmatrix}
1\\
5 \\
3\\
\end{pmatrix}
+
y\begin{pmatrix}
2\\
7 \\
1\\
\end{pmatrix}
+
z\begin{pmatrix}
3\\
8 \\
12\\
\end{pmatrix}
\end{equation*}

Why is this not the same for my example? Why are the columns in my rectangular matrix not 2D vectors?
 
Urmi Roy said:
Why are the columns in my rectangular matrix not 2D vectors?
Sounds weird when I just wrote
BvU said:
The 6 2D vectors you mention are the images of the 6 unit vectors in the 6D domain space.
So ##\ (1,0,0,0,0,0,0)\rightarrow(1,0)\ ##etc.
thereby calling them vectors, isn't it ?

Exactly analogous to your 3 x 3 example, where ##
\begin{pmatrix}
1\\
5 \\
3\\
\end{pmatrix}## is the image of unit vector ##
\begin{pmatrix}
1\\
0 \\
0\\
\end{pmatrix}##
 
BvU said:
You are mapping 6 variables (so a 6 dimensional space) onto 2 numbers (so a 2 dimensional space).
The 6 2D vectors you mention are the images of the 6 unit vectors in the 6D domain space.
So ##(1,0,0,0,0,0,0)\rightarrow(1,0)## etc.
These images are clearly not perpendicular: ##(0,1,0,0,0,0,0)\rightarrow(1,0)## also, etc.

With 6 DImensions and 2 constraints (your 2 equations) you have four degrees of freedom.
6D vectors that satisfy your equations look like e.g. ##(x-1, x_2, 1-x_1-x_2,x4,x_5,1-x_4-x_5)## so they are definitely not the axes.
Ok so thinking about this a bit more, I'm getting most of it. You say that since ##(1,0,0,0,0,0,0)\rightarrow(1,0)##
and also that ##(0,1,0,0,0,0,0)\rightarrow(1,0)##,
The 2D vectors are not perpendicular? I'm just struggling with the idea of why the 2D vectors in their 2D system aren't perpendicular. As I said, it's just easiest for me to imagine them as the x and y axes. Thanks for your help!
 
Yes, ##(1,0)## and ##(1,0)## are clearly one and the same and therefore not perpendicular. You can't find more than two mutually perpendicular vectors in 2D. As it happens the images of ##(1,0,0,0,0,0)## and ##(0,0,0,1,0,0)## are perpendicular, but you can't say: "the 6 unit vectors in the 6D domain of this matrix have images in the 2D range space that are perpendicular"
 
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