Understanding Rank One Matrices and Their Application in Nullspace and Row Space

In summary, the conversation discusses the principles of rank one matrices and their nullspace and row space. It also explains how the row reduced echelon form can be used to find the nullspace and row space of a matrix. The pivot columns of the row reduced form determine the columns that span the column space, while the null space can be found by solving the equation x+3y+10z=0.
  • #1
applechu
10
0
Hi:
I see an principle about rank one matrice in the book, and it says
if u=(1,2,3), [itex]\nu[/itex]t=[1 3 10], with Ax=0,
the equation [itex]\nu[/itex]tx=0;
The problem is I see an example like following:
s1=[-3
1
0]

s2=[-10
0
1]
The nullspace contains all combination of s1 and s2. and produces the plane
x+3y+10z=0, perpendicular to row(1,3,10). And it lead to the result
Nullspace perpendicular to row space. I didn't know what the result means and
how its imply, could anyone give me any instruct about that, thanks.
 
Last edited:
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  • #2
...go on.
 
  • #3
sorry, rewrite the s1, s2;

s1=\begin{pmatrix}
-3\\
1\\
0
\end{pmatrix}
and s2= \begin{pmatrix}
-10\\
0\\
1
\end{pmatrix}
 
  • #4
applechu said:
Hi:
I see an principle about rank one matrice in the book, and it says
if u=(1,2,3), [itex]\nu[/itex]t=[1 3 10], with Ax=0,
You defined u and [itex]\nu^t[/itex] but what is A and what is x?

the equation [itex]\nu[/itex]tx=0;
The problem is I see an example like following:
s1=[-3
1
0]

s2=[-10
0
1]
The nullspace contains all combination of s1 and s2. and produces the plane
x+3y+10z=0, perpendicular to row(1,3,10). And it lead to the result
Nullspace perpendicular to row space.
The "nullspace" and "row space"of what matrix?

I didn't know what the result means and
how its imply, could anyone give me any instruct about that, thanks.[/QUOTE]
 
  • #5
Hi:
A is a matrix, A=\begin{bmatrix}1 & 3 & 10\\2 & 6 & 20\\3 & 9 & 30\end{bmatrix}
u=(1,2,3) , Thanks
 
  • #6
applechu said:
Hi:
A is a matrix, A=\begin{bmatrix}1 & 3 & 10\\2 & 6 & 20\\3 & 9 & 30\end{bmatrix}
u=(1,2,3) , Thanks

The trick is to row reduce, we get

\begin{bmatrix}1 & 3 & 10\\0 & 0 & 0\\0 & 0 & 0\end{bmatrix}

So, let's think about this. To get to the reduced matrix, we can say we multiplied on the left by elementary matrices, leaving A to multiply any vector x on the other side. If x is is in the null space, in other words, if it solves Ax=0, wel then if and only if it's still in the null space after we multiplied by the elementary matrices. Therefore:

The nullspace of A is the same as that for the reduced echelon form.

For the row space, it's a little different, but the row reduced echelon form is helpful again. Since we were multiplying on the left, we changed the image, we change the column space. So the column space of A is not the same as the column space of the row reduced echelon form. But, the elementary matrices preserve linear dependence and linear independence, so the relations between columns is preserved. Blah blah, but what this means is:

The pivot columns of row reduced echelon form tell which columns of A span the column space of A.

So since the pivot columns of row reduced are just the leftmost column, the column space of A is spanned by it's left most column, that is (1,2,3).

For the null space of the row reduced echelon form, clearly it's where x+3y+10z=0. Trying z=0 and y=1 gives (-3,1,0), while y=0 and z=1 gives (-10,0,1).

Other problems may be more complicated, and ask for different forms of answers, but the two underlined sentences show how the row reduced form gets you closer to answers, and with a little cleverness, you can handle other problems.
 

What is a rank one matrix?

A rank one matrix is a matrix that can be written as the outer product of two vectors. This means that it has only one linearly independent row or column. In other words, it can be simplified to a matrix with only one non-zero row or column.

How is the rank of a matrix determined?

The rank of a matrix is determined by the number of linearly independent rows or columns. This can be calculated by using various methods such as Gaussian elimination or computing the matrix's determinant.

What are the properties of a rank one matrix?

Some properties of rank one matrices include having only one non-zero row or column, being symmetric (if the two vectors used in the outer product are the same), and having a determinant of 0 (if the two vectors are parallel).

Can a rank one matrix be inverted?

No, a rank one matrix cannot be inverted because it is not a square matrix and therefore does not have an inverse. Inverting a matrix requires it to be square and non-singular (having a non-zero determinant).

How is a rank one matrix used in linear algebra?

Rank one matrices are used in various applications of linear algebra, such as in solving systems of linear equations, calculating determinants, and finding eigenvalues and eigenvectors. They can also be used in data compression and image processing.

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