Intro to Linear Algebra - Nullspace of Rank 1 Matrix

In summary: Is the problem statement correct? In summary, the nullspace is a plane in R^n, but the nullspace isn't an n-1 dimensional space within R^n.
  • #1
fractalizard
2
0
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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  • #2
An [itex](n-1)[/itex] dimensional subspace in [itex]\mathbb{R}^n[/itex] is analagous to a plane in [itex]\mathbb{R}^3[/itex]: they are given by an equation of the form [itex]\mathbf{x} \cdot \mathbf{n} = 0[/itex].
 
  • #3
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.
 
  • #4
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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  • #5
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.
 
  • #6
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 [itex](n - 1)[/itex] dimensional subspace is [tex]\{ \mathbf{x} \in \mathbb{R}^n: \mathbf{x} \cdot \mathbf{a} = x_1a_1 + \cdots + x_na_n = 0\}[/tex] for some [itex]\mathbf{a} \neq 0[/itex]. This is analagous to a plane in [itex]\mathbb{R}^3[/itex]. In higher dimensions one might more properly call it a hyperplane.
 
  • #7
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!)
 
  • #8
pasmith said:
As I stated above, every [itex](n - 1)[/itex] dimensional subspace is [tex]\{ \mathbf{x} \in \mathbb{R}^n: \mathbf{x} \cdot \mathbf{a} = x_1a_1 + \cdots + x_na_n = 0\}[/tex] for some [itex]\mathbf{a} \neq 0[/itex]. This is analagous to a plane in [itex]\mathbb{R}^3[/itex]. 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.
 
  • #9
Office_Shredder said:
I would think of a plane with no prefix as being 2 dimensions.
This...
 

1. What is a nullspace?

A nullspace, also known as a kernel, is the set of all vectors that when multiplied by a given matrix result in a zero vector. In other words, it is the set of vectors that are mapped to the origin by the matrix.

2. What is a rank 1 matrix?

A rank 1 matrix is a matrix where all of its columns are multiples of each other. This means that the columns are linearly dependent, and the rank of the matrix is 1.

3. How is the nullspace of a rank 1 matrix related to its columns?

The nullspace of a rank 1 matrix is the set of all vectors that are orthogonal to its columns. This is because the columns are multiples of each other, and therefore span the same subspace. Any vector that is orthogonal to the columns will be in the nullspace.

4. Can the nullspace of a rank 1 matrix contain more than one vector?

No, the nullspace of a rank 1 matrix can only contain one vector. This is because the rank 1 matrix only has one linearly independent column, and the nullspace is the set of all vectors that are orthogonal to this column.

5. How can the nullspace of a rank 1 matrix be used in applications?

The nullspace of a rank 1 matrix can be used to find solutions to systems of linear equations. This is because the nullspace contains all of the vectors that satisfy the homogeneous equation Ax=0, where A is the rank 1 matrix. It can also be used in data compression and image processing, where it can help identify patterns and reduce the dimensionality of data.

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