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

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Rank one matrices have a nullspace that is perpendicular to their row space, as illustrated by the example with matrix A and vectors s1 and s2. The row reduction of A reveals that the nullspace consists of all combinations of s1 and s2, leading to the equation x + 3y + 10z = 0. This indicates that the nullspace is spanned by the vectors that form a plane perpendicular to the row space defined by the first row of A. The relationship between the nullspace and row space is crucial for understanding linear transformations and their properties. Overall, the discussion emphasizes the importance of row reduction in analyzing the nullspace and row space of matrices.
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Hi:
I see an principle about rank one matrice in the book, and it says
if u=(1,2,3), \nut=[1 3 10], with Ax=0,
the equation \nutx=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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sorry, rewrite the s1, s2;

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

the equation \nutx=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]
 
Hi:
A is a matrix, A=\begin{bmatrix}1 & 3 & 10\\2 & 6 & 20\\3 & 9 & 30\end{bmatrix}
u=(1,2,3) , Thanks
 
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.
 
I am studying the mathematical formalism behind non-commutative geometry approach to quantum gravity. I was reading about Hopf algebras and their Drinfeld twist with a specific example of the Moyal-Weyl twist defined as F=exp(-iλ/2θ^(μν)∂_μ⊗∂_ν) where λ is a constant parametar and θ antisymmetric constant tensor. {∂_μ} is the basis of the tangent vector space over the underlying spacetime Now, from my understanding the enveloping algebra which appears in the definition of the Hopf algebra...

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