Row and null complements of x; need clarity....

In summary, we can always split a vector into its nullspace and row space components, as the null and row spaces are orthogonal complements.
  • #1
kostoglotov
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I've managed to distill the rambling into just this question, posted here and at the end of my digressive thoughts as well:

"Will we always be able to split x up in such a way that we have a nullspace component and a non-row space component?"

Take a matrix
[tex]A = \begin{bmatrix}1 & 2\\ 3 & 6\end{bmatrix} \ \text{with} \ \vec{x}=\begin{bmatrix} 4 \\ 3 \end{bmatrix}[/tex]

Split [itex]\vec{x}[/itex] into it's null-space and row-space components.

Now, the example in the text is fine

First splitting of x:

[tex]\begin{bmatrix}1 & 2\\ 3 & 6\end{bmatrix}\left(\begin{bmatrix} 2 \\ -1 \end{bmatrix}+\begin{bmatrix} 2 \\ 4 \end{bmatrix}\right) = \begin{bmatrix} 10 \\ 30 \end{bmatrix}[/tex]

I can easily see that one x vec is in the nullspace and one x vec used is in the row space.

But this other set of two x's also works

Second splitting of x:

[tex]\begin{bmatrix}1 & 2\\ 3 & 6\end{bmatrix}\left(\begin{bmatrix} -2 \\ 1 \end{bmatrix}+\begin{bmatrix} 6 \\ 2 \end{bmatrix}\right) = \begin{bmatrix} 10 \\ 30 \end{bmatrix}[/tex]

The first vec in this second splitting is still in the nullspace, but the second one is not in the row-space.

After all, the row space of A, [itex]C(A^T)[/itex] just contains combinations of the vector

[tex]\begin{bmatrix} 1 \\ 2 \end{bmatrix}[/tex]

I can't see how any linear combination of that vector could produce the (6,2) vec I used in the second splitting of x.

Is this a problem? I don't see how it could be since both splittings produce the same answer, an answer that is in the column space of A.

However, this is also taking place in [itex]R^2[/itex], and by the fundamental theorem, shouldn't that whole space be partitioned by the subspaces? NO...that's dumb. Row space is just a line through zero in the xy and nullspace is a line through zero in xy perp to row space...that's not a partition, it's subsets.

But does that THEN mean, that any vector in xy, that is NOT in the null space, always dot products with row space vectors to produce vectors in the column space? Yes...of course...it has to.

Will we always be able to split x up in such a way that we have a nullspace component and a non-row space component?
 
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  • #2
kostoglotov said:
Will we always be able to split x up in such a way that we have a nullspace component and a non-row space component?
Yes, because the null and row spaces are orthogonal complements, and hence have intersection {0}.

So if we have ##\vec{v}=\vec{n}+\vec{r}## where the two vectors on the RHS are in the null and row spaces respectively, then we can write ##\vec{v}=2\vec{n}+(\vec{r}-\vec{n})## and the first vector on the RHS is in the nullspace but the second is in neither the null nor the row space (the proof of that is left as an exercise).
 
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FAQ: Row and null complements of x; need clarity....

What are row and null complements?

Row and null complements are mathematical concepts used in linear algebra. The row complement of a vector or matrix x is a vector or matrix that when multiplied by x, produces a result of zero. The null complement is the set of all vectors that produce a zero result when multiplied by x.

Why is it important to understand row and null complements?

Understanding row and null complements is important because they are fundamental concepts in linear algebra and have many applications in fields such as physics, engineering, and computer science. They are also essential for solving systems of linear equations and finding solutions to problems in data analysis and optimization.

How do you find the row and null complements of a given matrix?

The row complement of a matrix x can be found by taking the transpose of x and finding the null space (or kernel) of the resulting matrix. The null complement can be found by finding the null space of x directly.

Can row and null complements be the same?

No, row and null complements cannot be the same because they are defined as complementary sets. In other words, the row complement contains all the vectors that are orthogonal (perpendicular) to the rows of x, while the null complement contains all the vectors that are orthogonal to the columns of x.

How can understanding row and null complements be useful in practical applications?

Understanding row and null complements can be useful in many practical applications such as image and signal processing, where the null complement can be used to remove noise from a signal, and in data compression, where the row complement can be used to reduce the dimensionality of a dataset without losing important information.

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