Intro to Linear Algebra - Nullspace of Rank 1 Matrix

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The discussion revolves around the nullspace of a rank 1 matrix and its dimensionality. It clarifies that the nullspace is indeed an (n-1) dimensional subspace within R^n, often referred to as a hyperplane in higher dimensions. For example, a 1x2 matrix has a nullspace represented by a line, while a 2x2 matrix of rank 1 has a nullspace that is a line in R^2. The confusion arises from terminology, as the term "plane" is sometimes used to describe these subspaces, which can lead to misunderstandings. Ultimately, the nullspace's dimensionality aligns with the mathematical definition of subspaces in linear algebra.
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Homework Statement
Given A is a mxn matrix with rank 1, the nullspace is a _____ in R^n.
Relevant Equations
N(A) = Linear combination of "special solutions" to A
The published solutions indicate that the nullspace is a plane in R^n. Why isn't the nullspace an n-1 dimensional space within R^n? For example, if I understand things correctly, the 1x2 matrix [1 2] would have a nullspace represented by any linear combination of the vector (-2,1), which would be a line.
 
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An (n-1) dimensional subspace in \mathbb{R}^n is analagous to a plane in \mathbb{R}^3: they are given by an equation of the form \mathbf{x} \cdot \mathbf{n} = 0.
 
fractalizard said:
Homework Statement: Given A is a mxn matrix with rank 1, the nullspace is a _____ in R^n.
Relevant Equations: N(A) = Linear combination of "special solutions" to A

The published solutions indicate that the nullspace is a plane in R^n.
That sounds wrong. Are you sure that you copied the problem statement exactly? The problem statement or the book answer might have a typo. Or the book might just need some more proofreading. It's very hard to eliminate all errors from a book.
fractalizard said:
Why isn't the nullspace an n-1 dimensional space within R^n?
It is. You can verify the properties of a subspace.
 
fractalizard said:
Homework Statement: Given A is a mxn matrix with rank 1, the nullspace is a _____ in R^n.
Relevant Equations: N(A) = Linear combination of "special solutions" to A

The published solutions indicate that the nullspace is a plane in R^n. Why isn't the nullspace an n-1 dimensional space within R^n? For example, if I understand things correctly, the 1x2 matrix [1 2] would have a nullspace represented by any linear combination of the vector (-2,1), which would be a line.
Perhaps the authors of the solution meant that the nullspace was a hyperplane; an object of dimension one less than that of ##\mathbb R^n##.

For a 2x2 matrix of rank 1, the domain is ##\mathbb R^2## and the nullspace is of dimension 1, so the nullspace is a line in ##\mathbb R^2##.

For a 3x4 matrix of rank 1, the domain is ##\mathbb R^4## and the nullspace is of dimension 3, so the nullspace is a hyperplane in ##\mathbb R^4##.
 
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Wow, this place is great - thanks for the quick replies. I suspected that this might be an error in the solution. FYI, my posted problem statement was only the piece of the original problem that I was having trouble with, here is the full problem statement:

40 only.png
And from the solution manual:

40 answer.png


The highlighted line is the piece I am having trouble understanding, the rest of the solution makes sense to me.
 
fractalizard said:
The highlighted line is the piece I am having trouble understanding, the rest of the solution makes sense to me.

As I stated above, every (n - 1) dimensional subspace is \{ \mathbf{x} \in \mathbb{R}^n: \mathbf{x} \cdot \mathbf{a} = x_1a_1 + \cdots + x_na_n = 0\} for some \mathbf{a} \neq 0. This is analagous to a plane in \mathbb{R}^3. In higher dimensions one might more properly call it a hyperplane.
 
fractalizard said:
And from the solution manual:

View attachment 326075
The highlighted line is the piece I am having trouble understanding, the rest of the solution makes sense to me.
Hi @fractalizard. Welcome to PF.

In addition to the other excellent replies I’d like to add a (non-mathematician's) example which might help.

Say ##A## is a ##4 \times 4## matrix. If the rank = 1 then any row is a scalar multiple of any other row, For example:

##A = \begin {bmatrix}
a&b&c&d \\
3a&3b&3c&3d\\
-2a&-2b&-2c&-2d\\
4a&4b&4c&4d
\end{bmatrix}##

So in this example we have rows: ##R_2=3R_1, ~R_3= -2R_1## and ##R_4 =4R_1##.

Take the vector ##\textbf {x}=\begin {bmatrix} x_1\\x_2\\x_3\\x_4 \end {bmatrix}##

##A \textbf {x}=\begin {bmatrix}
R_1\cdot \textbf {x}\\
3R_1\cdot \textbf {x}\\
-2R_1\cdot \textbf {x}\\
4R_1\cdot \textbf {x}
\end {bmatrix}##

To make ##A \textbf {x}=0 ## (i.e. for ##\textbf {x}## to be in A’s null space) we require only that ##R_1\cdot \textbf {x} = 0##.

This gives the required ‘single equation’ (mentioned in the soution manual): ##ax_1 + bx_2 + cx_3 + dx_4= 0##

This equation defines a ‘plane’ in ##\mathbb R^n##. Though, as already has been said, for n>3 the term ‘hyperplane’ could be used to avoid confusion with 2D planes. (Also, note that for n = 2, the so-called ‘plane’ would a line!)
 
pasmith said:
As I stated above, every (n - 1) dimensional subspace is \{ \mathbf{x} \in \mathbb{R}^n: \mathbf{x} \cdot \mathbf{a} = x_1a_1 + \cdots + x_na_n = 0\} for some \mathbf{a} \neq 0. This is analagous to a plane in \mathbb{R}^3. In higher dimensions one might more properly call it a hyperplane.

The same way a line is always 1 dimension, I would think of a plane with no prefix as being 2 dimensions.
 
Office_Shredder said:
I would think of a plane with no prefix as being 2 dimensions.
This...