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### Course: Linear algebra > Unit 2

Lesson 7: Transpose of a matrix- Transpose of a matrix
- Determinant of transpose
- Transpose of a matrix product
- Transposes of sums and inverses
- Transpose of a vector
- Rowspace and left nullspace
- Visualizations of left nullspace and rowspace
- rank(a) = rank(transpose of a)
- Showing that A-transpose x A is invertible

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# Visualizations of left nullspace and rowspace

Relationship between left nullspace, rowspace, column space and nullspace. Created by Sal Khan.

## Want to join the conversation?

- Sorry my poor English

I don't understand why does Sal draw left nullspace in codomain and rowspace in domain.

As far as I know, the nullspace belongs to domain because it's the subspace of the domain that makes all vectors 0 in the codomain. And column space spans all the linear combinations of the column vectors in the codomain.

But why does left null space belong to the codomain? apart from in this particular example the left null space and the domain both are 3rd dimension.(3 votes)- The domain of the transformation "Ax" in this video is R^3, the codomain R^2 (Because A is 2x3).

The domain of "((A)T)z" ("A transpose times z") is R^2, the codomain R^3 (Because (A)T is 3x2).

This means that the domain of the transformation for the left nullspace of A is the codomain of that for the nullspace of A. What follows here is more detail:

We know that the transpose of a matrix product "AB" is equal to ((B)T)(A)T (expained four videos ago), and so the left N(A) is equal to N((A)T), because:

if ((A)T)z = 0, then [((A)T)z]T = (0)T = ((z)T)[((A)T)T] = ((z)T)A.

The equation for the left nullspace in either of the above forms, "((A)T)z = 0" or "((z)T)A = (0)T" has the same result (transforms an R^2 vector into the 0 vector in R^3), and so is a transformation from R^2 (the codomain of A) to R^3 (the domain of A) even though "((z)T)A" has A in it and not (A)T.

I found it helpful to write out all three systems of equations in vector form ("Ax = 0", "((A)T)z = 0", and "((z)T)A = (0)T"), using the matrix A in this video, and using the correct row and column vectors, then find the solutions with just matrix multiplication (not using augmented matrices, which work only for the first two).(2 votes)

- It seems that the dimenison of the column space = number of rows and the dimension of the row space = number of columns. Does that always happen? Where did he prove this?(2 votes)
- This does not always happen. Simple example:

[1 0 0]

[0 1 0]

[0 0 0]

has three rows, but the dimension of the column space is only two. The row space has dimension 2, but there are three columns.

What is however true is the Rank Nullity Theorem, which says the following:

Let A be an mxn matrix. Then dim(Column Space) + dim(Null Space) = n.

Some proofs of this theorem are very long, but you can definitely find one on google somewhere if you want.(3 votes)

- Sal said he would make a proof for how N(A^T) and C(A) are othogonal in the next video. He didn't. Perhaps he has done it in the next playlist, although I haven't got that far yet. Regardless, I thought I'd share my proof of it anyway.

Let the matrix A = [**a1****a2**...**an**], where**ai**are column vectors.

The column space of A, C(A), is the span of all the vectors**ai**.

The nullspace of A^T, or the left nullspace of A, is the set of all vectors**x**such that A^T**x**=**0**. This is hard to write out, but A^T is a bunch of row vectors**ai**^T.

Performing the matrix-vector multiplication, A^T**x**=**0**is the same as**ai**dot**x**= 0 for all**ai**.

This means that**x**is orthogonal to every vector**ai**.

This means that every member of the left nullspace of A is orthogonal to each of the column vectors of A.

Because the dot product is a linear operation, this means that every member of the left nullspace of A is orthogonal to every linear combination of the column vectors of A, i.e. every member of the span of the column vectors of A.

Therefore, N(A^T) is orthogonal to C(A).

QED.(2 votes)- I looked at it this way - unless I'm missing something, it's not an oversimplification:

C(A) and the rowspace of A^t ("R(A^t)") are identical. Since N(A^t) is orthogonal to "R(A^t)", it's also orthogonal to C(A). QED.(1 vote)

- Is this another way of stating what's in the video?

Given Amxn, the preimage of 0 in R^n under A^T is the "orthogonal complement" of the image of A.(2 votes)- The preimage of 0 under "(A)T (A transpose)" is in R^m.

So "The preimage of the 0 vector in R^m under (A)T is the orthogonal complement of the image of A.

Or, "N((A)T) = the orthogonal complement of the rowspace of (A)T.(0 votes)

- As far as I know, the column space of a matrix is equal to the its row space. Doesn't this equality have implications on the equalities derived in this video? Namely:

1)row space(A) orthog on N(A) <=> C(A^T) orthog on left N(A^T)

2)C(A) orthog on left N(A) <=> row space (A^T) orthog on N(A^T)(1 vote)- Well the column space is not equal to the row space. They don't even need to have the same number of elements -- as shown in this example in the video.(1 vote)

- I realized that the cross product of the two vector that spans N(A) is (1, -1/2, -3/2), which is one vector of C(At), which is the span of (2, -1, -3). And the graph that Sal drew also suggest a relationship to cross product. Is that correct that the cross product of the span of N(A) is always part of C(At)? And what is the deeper meaning of this? Thank you in advance!(1 vote)
- The cross product always returns a vector which is othogonal to the input vectors. So, if N(A) has dimension 2 and is a member of R3, taking the cross product of two independent vectors within N(A) will give you a vector in C(At), because C(At) is always orthogonal to N(A).

However, this observation only applies if N(A) has dimension 2 and is a member of R3, otherwise it is not possible to take the cross product of two linearly independent vectors in N(A) to obtain a vector outside N(A).(1 vote)

- Interesting video. A bit convoluted and overextended. Would my final conclusion be correct?

Finding the Nullspace(Transpose(A)) is the same as trying to figure out some set of vectors V that are an orthogonal complement of the row vectors of the original matrix A.(1 vote) - in this situation, left space and column space are the lines. However, there exists any situations that both of them are R2 or one of them is R2?

If it does, i think they are not orthogonal, right??

p/s: sorry for my bad grammar(1 vote)- The left null space = N((A)T) (the null space of A transpose). C(A) = the row space of (A)T.

As you will see later, the row space and null space of a matrix are always complementary. The domain of (A)T is R^2 so dim(N((A)T)) + dim(Rowspace((A)T)) = 1 + 1 = 2.

If the domain of a matrix is R^4, its row space and null space dimensions must add up to four. So both could be two dimensional. If the domain is 3 dimensional (as in A in this video, where dim(N(A)) is 2, and dim(R(A)) is 1, they must add to three (as this video shows), and n; n.(1 vote)

- What was he referring to when he said there was a mapping from R3 to R2?

How did he come up with this?(1 vote) - What does "complement" mean in terms of "orthogonal complement"?10:39(1 vote)

## Video transcript

In the last video I had this 2
by 3 matrix A right here, and we figured out all of the
subspaces that are associated with this matrix. We figured out its null space,
its column space, we figured out the null space and column
space of its transpose, which you could also call the left
null space, and the row space, or what's essentially the space
spanned by A's rows. Let's write it all in one place,
because I realize it got a little disjointed, and see
if we can visualize what all of these look
like, especially relative to each other. So let me copy and paste
my original matrix. Copy, and then let me scroll
down here and paste it over here, and hit paste. Let me see if I can find
our key takeaways from the last video. So our column space right here,
of A, was this thing right here. Let me write this. This was our column space. It was the span of the
R2 vector 2, 4. Let me copy that. Copy that and bring it down. Hit paste. This was our column space. Let me write that. This is the column space of A. It was equal to that
right there. And now what other things
do we know? Well, we know that the left null
space was a span of 2, 1. Let me write that. So our left null space, or the
null space of our transpose, either way, it was equal to the
span of the R2 vector 2, 1, just like that. And then what was
our null space? Our null space we figured
out in the last video. Here it is. It's the span of these
two R3 vectors. Let me copy and paste that. Hit copy. Let me go down here. Let me paste it. So that was our null
space right there. And then finally, what
was our row space? What was our row space or the
column space of our transpose? So the column space of our
transpose was the span of this R3 vector right there, So it
was this one right here. So let me copy and paste it. Copy and scroll down, and we can
paste it just like that. OK, let's see if we can
visualize this now, now that we have them all in one place. So first of all, if we imagine
a transformation, x, that is equal to A times x, our
transformation is going to be a mapping from what? x would be
a member of R3, so R3 would be our domain. So it would be a mapping from
R3 and then it would be a mapping to R2 because we have
two rows here, right? You multiply a 2-by-3 matrix
times a 3-by-1 vector, and you're going to get a 2-by-1
vector, so it's going to be a mapping to R2. So that's our codomain. So let's draw our domains
and our codomains. I'll just write them very
generally right here. So you could imagine
R3 is our domain. And then our codomain is going
to be R2 just like that. And our T is a mapping, or you
could even imagine A is a mapping between any vector there
and any vector there when you multiply them. Now, what is our column
space of A? Our column space of A is the
span of the vector 2 minus 4. It's an R2 vector. This is a subspace of R2. We could write this. So let me write this. So our column space of A, these
are just all of the vectors that are spanned
by this. We figured out that these guys
are just multiples of this first guy, or we could have
done it the other way. We could have said this guy and
that guy are multiples of that guy, either way. But the basis is just one
of these vectors. We just have to have one of
these vectors, and so it was equal to this right here. So the column space
is a subset of R2. And what else is
a subset of R2? Well, our left null space. Our left null space is
also a subset of R2. So let's graph them, actually. So I won't be too exact,
but you can imagine. Let's see, if we draw the
vector 2, 4-- let me draw some axes here. Let me scroll down
a little bit. So if you have some vector--
let me draw my-- do this as neatly as possible. That's my vertical axis. That is my horizontal axis. And then, what does the span of
our column space look like? So you draw the vector 2, minus
4, so you're going to go out one, two, and then you're
going to go down one, two, three, four. So that's what that
vector looks like. And the span of this vector
is essentially all of the multiples of this vector, where
you could say linear combinations of it, but you're
taking a combination of just one vector, so it's just going
to be all of the multiples of this vector. So if I were to graph it, it
would just be a line that is specified by all of the linear
combinations of that vector right there. This right here is a graphical
representation of the column space of A. Now, let's look at the left null
space of A, or you could imagine, the null space
of the transpose. They are the same thing. You saw why in the last video. What does this look like? So the left null space
is a span of 2, 1. So if you graph 2, and then you
go up 1, it's the graph of 2, 1, and it looks like this. Let me do it in a
different color. So that's what the vector
looks like. The vector looks like that,
but of course, we want the span of that vector, so it's
going to be all of the combinations. All you can do when you combine
one vector is just multiply it by a bunch of
scalars, so it's going to be all of the scalar multiples
of that vector. So let me draw it like that. It's going to be like that. And the first thing you might
notice, let me write this. This is our left null space
of A or the null space of our transpose. This is equal to the left
null space of A. And actually, since we're
writing, we wrote this in terms of A transpose. It's the null space of A
transpose, which is the left null space of A. Let's write the column
space of A also in terms of A transpose. This is equal to the row space
of A transpose, right? If you're looking at the columns
of A, everything it spans, the columns of A are the
same things as the rows of A transpose. But the first thing that you
see, when I just at least visually drew it like this, is
that these two spaces look to be orthogonal to each other. It looks like I drew it in R2. It looks like there's a
90-degree angle there. And if we wanted to verify it,
all we have to do is take the dot product. Well, any vector that is in our
column space, you could take an arbitrary vector that's
in our column space, it's going to be equal
to c times 2 minus 4. So let me write that down. I want this stuff up here. I'll scroll down a little bit. Let's say v1 is a member
of our column space. And that means that v1 is going
to be equal to some scalar multiple times the
spanning vector of our column space, so some scale or
multiple of this. So we could say it's equal
to c1 times 2 minus 4. That's some member of
our column space. Now, if we want some member of
our left null space-- let's write it here. So let's say that v2 is some
member of our left null space, or the null space of
the transpose, then what does that mean? That means v2 is going to be
equal to some scalar multiple of the spanning vector of our
left null space of 2, 1. So any vector that's in our
column space could be represented this way. Any vector in our left
null space can be represented this way. Now, what happens if you take
the dot product of these two characters? So let me do it down here. I want to save some space for
what we're going to do in R3, but let me take the dot
product of these two characters. So v1 dot v2 is equal to--
I'll arbitrarily switch colors-- c1 times 2 minus
4 dot c2 times 2, 1. And then the scalars, we've
seen this before. You can just say that this is
the same thing as c1, c2 times the dot product of 2
minus 4 dot 2, 1. And then what is
this equal to? This is going to be equal to
c1, c2 times 2 times 2 is 4 plus minus 4 times 1: minus 4. Well, this is going to be equal
to 0, so this whole expression is going
to be equal to 0. And this was for any two vectors
that are members of our column space and our
left null space. They're orthogonal
to each other. So every member of our column
space is going to be orthogonal to every member of
our left null space, or every member of the null space of our
transpose, and that was the case in this example. It actually turns out this is
always going to be the case, that your column space of
a matrix, its orthogonal complement is the left null
space, or the null space of its transpose. I'll prove that probably in the
next video, either in the next video or the video after
that, but you can see it visually for this example. Now let's draw the other two
characters that we're dealing with here. So we have our null space, which
is the span of these two vectors in R3. It's a little bit more difficult
to draw it, these two vectors in R3 right there. But what is the span of
two vectors in R3? All of the linear combinations
of two vectors in R3 is going to be a plane in R3. So I'll draw it in just very
general terms right here. If we draw it in just very
general terms, let me see. So it's a plane in R3 that
looks like that. Maybe I'll fill in the plane
a little bit, give you some sense of what it looks like. This is the null space of A. It's spanned by these
two vectors. Now, you could imagine these
two vectors look something like-- I'm drawing it very
general, but if you take any linear combinations of these two
guys, you're going to get any vector that's along this
plane that goes in infinite directions. And, of course, the origin
will be in these. All of these are valid
subspaces. Now, what does the row
space of A look like? Or you could say the column
space of A transpose? Well, it's the span of this
vector in R3, but let's see something interesting about
this vector in R3. How does it relate to
these two vectors? Well, you may not see it
immediately, although if you look at it closely, it might pop
out at you, that this guy is orthogonal to both
of these guys. Notice, if you take the dot
product of 2 minus 1 minus 3, and you dotted it with 1/2, 1,
0, what are you going to get? You're going to get 2 times 1/2,
which is 1, plus minus 1 times 1, which is minus
1, plus minus 3 times 0, which is 0. So that's when I dotted that guy
with that guy right there. And then, when I take the dot
of this guy with that guy, what do you get? You get 3/2, 0 and 1, dotted
with-- let me scroll down a little bit. I don't want to write too
small-- dotted with 1, dotted with 2 minus 1 minus 3. In the row space of A,
I wrote the spanning vector there this time. I probably shouldn't have
switched the order. But here, I'm dotting it with
this guy, and then here, I'm dotting it with this
guy right there. So if you take it, 3/2 times 2
is equal to 3 plus 0 times minus 1 is 0, plus 1 times minus
3 is minus 3, so it's equal to 0. So the fact that this guy is
orthogonal to both of these spanning vectors, it also means
that it's orthogonal to any linear combination
of those guys. Maybe it might be useful
for you to see that. So let's take some member
of our null space. So let's say the vector v3 is
a member of our null space. That means it's a linear
combination of that guy and that guy. Those are the two spanning
vectors. I'd written it up here. These are our two spanning
vectors. I need the space down here,
so let me scroll down a little bit. These are the two spanning
vectors. So that means that v3 can be
written as some linear combination of these two guys
that I squared off in pink. So let me just write it as maybe
A times 3/2, 0, 1 plus b times 1/2, 1, 0. Now, what happens if I take the
dot product of v3 and I dot it with any member of
my row space right here? So any member of my row space
is going to be a multiple of this guy right here. That is the spanning vector
of my row space. Just let me actually
create that. So let me say that v4 is a
member of my row space, which is the column space of
the transpose of A. And that means that v4 is equal
to, let's say, some scaling vector. I always use c a lot. Let me use d. Let's say it's d times
my spanning vector. d times 2 minus 1, 3. So what is v3, which is just
any member of my null space dotted with v4, which is any
member of my row space? So what is this going
to be equal to? This is going to be
equal to this guy. So let me write it like this. A times 3/2, 0, 1 plus v times
1/2, 1, 0 dotted with this guy, dot d times 2 minus 1, 3. Now, what is this going
to be equal to? Well, we know all of
the properties of vector dot products. We can distribute it and then
take the scalars out. So this is going to be equal
to-- I'll skip a few steps here, but it's going to be equal
to-- ad times the dot product of 3/2, 0, 1, dot 2
minus 1, 3-- just distribute it out to here-- plus bd times
the dot product of 1/2, 1, 0, dotted with 2 minus 1, 3. This is the dot product. I just distributed this
term along these two terms right here. And we already know what these
dot products are equal to. We did it right here. This dot product right here
is that dot product. I just switched the order,
so this is equal to 0. And this dot product is that dot
product, so this is also equal to 0. So you take any member of your
row space and you dot it with any member of your null space,
and you're going to get 0, or any member of your row space is
orthogonal to any member of your null space. And I did all of that to
help our visualization. So we just saw that any member
of our row space, which is the span of this vector, is
orthogonal to any member of or null space. So my row space, which is just
going to be a line in R3 because it's just a multiple
of a vector. It's going to look like this. It's going to be a line,
and then it's going to maybe go behind it. You can't see it there. It's going to look like that,
but it's going to be orthogonal. So let me draw it. So this pink line right here in
R3, that is our row space of A, which is equal to the
column space of A transpose because the rows of A are the
same thing as the columns of A transposed, and the row space
is just the space spanned by your row vectors. And then this is the null space
of A, which is a plane. It's spanned by two
vectors in R3. Or we could also call
that the left null space of A transpose. And I never used this term in
the last video, but it's symmetric, right? If the null space of A transpose
is the left null space of A, then the null space
of A is the left null space of A transpose, which is
an interesting takeaway. Notice that you have here the
row space of A is orthogonal to the null space of A. And here, you have the row
space of A transpose is orthogonal to the null
space of A transpose. Or you could say the left null
space of A is orthogonal to the column space of A. Or you could say the left null
space of A transpose is orthogonal to the column
space of A transpose. So these are just
very interesting takeaways, in general. And just like I said here, that
look, these happen to be orthogonal. These also happen to
be orthogonal. And this isn't just some
strange coincidence. In the next video or two, I'll
show you that this space, this pink space, is the orthogonal
complement of the null space right here, which means it
represents all of the vectors that are orthogonal
to the null space. And these two guys
are orthogonal complements to each other. They each represent all of the
vectors that are orthogonal to the other guy in their
respective spaces.