I've been multiplying
matrices already,
but certainly time for
me to discuss the rules
for matrix multiplication.
And the interesting part is
the many ways you can do it,
and they all give
the same answer.
And they're all important.
So matrix multiplication,
and then, come inverses.
So we mentioned the
inverse of a matrix.
That's a big deal.
Lots to do about inverses
and how to find them.
Okay, so I'll begin with how
to multiply two matrices.
First way, okay, so suppose
I have a matrix A multiplying
a matrix B and --
giving me a result --
well, I could call it C.
A times B.
Okay.
So, let me just review
the rule for this entry.
That's the entry in
row i and column j.
So that's the i j entry.
Right there is C i j.
We always write the row number
and then the column number.
So I might --
I might -- maybe I take it C
3 4, just to make it specific.
So instead of i j,
let me use numbers.
C 3 4.
So where does that come
from, the three four entry?
It comes from row three, here,
row three and column four,
as you know.
Column four.
And can I just write
down, or can we
write down the formula for it?
If we look at the whole
row and the whole column,
the quick way for me to
say it is row three of A --
I could use a dot
for dot product.
I won't often use
that, actually.
Dot column four of B.
But this gives us a
chance to just, like,
use a little matrix notation.
What are the entries?
What's this first
entry in row three?
That number that's
sitting right there is...
A, so it's got two
indices and what are they?
3 1.
So there's an a 3 1 there.
Now what's the first guy
at the top of column four?
So what's sitting up there?
B 1 4, right.
So that this dot product
starts with A 3 1 times B 1 4.
And then what's the next -- so
this is like I'm accumulating
this sum, then comes the next
guy, A 3 2, second column,
times B 2 4, second row.
So it's b A 3 2,
B 2 4 and so on.
Just practice with indices.
Oh, let me even practice
with a summation formula.
So this is --
most of the course,
I use whole vectors.
I very seldom, get
down to the details
of these particular entries,
but here we'd better do it.
So it's some kind
of a sum, right?
Of things in row three,
column K shall I say?
Times things in
row K, column four.
Do you see that that's
what we're seeing here?
This is K is one, here
K is two, on along --
so the sum goes all the
way along the row and down
the column, say, one to N.
So that's what the C three
four entry looks like.
A sum of a three K b K four.
Just takes a little
practice to do that.
Okay.
And -- well, maybe
I should say --
when are we allowed to
multiply these matrices?
What are the shapes
of these things?
The shapes are --
if we allow them to be not
necessarily square matrices.
If they're square,
they've got to be the same
size.
If they're rectangular,
they're not the same size.
If they're rectangular,
this might be -- well,
I always think of A as m by n.
m rows, n columns.
So that sum goes to n.
Now what's the point -- how
many rows does B have to have?
n.
The number of rows in
B, the number of guys
that we meet coming down has
to match the number of ones
across.
So B will have to
be n by something.
Whatever.
P. So the number of columns here
has to match the number of rows
there, and then
what's the result?
What's the shape of the result?
What's the shape
of C, the output?
Well, it's got these same
m rows -- it's got m rows.
And how many columns?
P. m by P. Okay.
So there are m times P little
numbers in there, entries,
and each one, looks like that.
Okay.
So that's the standard rule.
That's the way people think
of multiplying matrices.
I do it too.
But I want to talk
about other ways
to look at that
same calculation,
looking at whole
columns and whole rows.
Okay.
So can I do A B C again?
A B equaling C again?
But now, tell me about...
I'll put it up here.
So here goes A, again,
times B producing C.
And again, this is m by n.
This is n by P and
this is m by P. Okay.
Now I want to look
at whole columns.
I want to look at
the columns of --
here's the second way
to multiply matrices.
Because I'm going to build
on what I know already.
How do I multiply a
matrix by a column?
I know how to multiply
this matrix by that column.
Shall I call that column one?
That tells me column
one of the answer.
The matrix times the first
column is that first column.
Because none of
this stuff entered
that part of the answer.
The matrix times
the second column
is the second column
of the answer.
Do you see what I'm saying?
That I could think of
multiplying a matrix
by a vector, which I
already knew how to do,
and I can think of just P
columns sitting side by side,
just like resting
next to each other.
And I multiply A times
each one of those.
And I get the P
columns of the answer.
Do you see this as --
this is quite nice,
to be able to think, okay,
matrix multiplication works
so that I can just think
of having several columns,
multiplying by A and getting
the columns of the answer.
So, like, here's column one
shall I call that column one?
And what's going in there
is A times column one.
Okay.
So that's the picture
a column at a time.
So what does that tell me?
What does that tell me
about these columns?
These columns of C
are combinations,
because we've seen that
before, of columns of A.
Every one of these
comes from A times this,
and A times a vector
is a combination
of the columns of A.
And it makes sense, because
the columns of A have length m
and the columns of
C have length m.
And every column of
C is some combination
of the columns of A.
And it's these
numbers in here that
tell me what combination it is.
Do you see that?
That in that answer, C,
I'm seeing stuff that's
combinations of these columns.
Now, suppose I look at it
-- that's two ways now.
The third way is
look at it by rows.
So now let me change to rows.
Okay.
So now I can think
of a row of A --
a row of A multiplying all these
rows here and producing a row
of the product.
So this row takes a
combination of these rows
and that's the answer.
So these rows of C are
combinations of what?
Tell me how to finish that.
The rows of C, when I have a
matrix B, it's got its rows
and I multiply by A,
and what does that do?
It mixes the rows up.
It creates combinations
of the rows of B, thanks.
Rows of B.
That's what I wanted to
see, that this answer --
I can see where the
pieces are coming from.
The rows in the answer are
coming as combinations of these
rows.
The columns in the answer are
coming as combinations of those
columns.
And so that's three ways.
Now you can say, okay,
what's the fourth way?
The fourth way --
so that's -- now we've
got, like, the regular way,
the column way,
the row way and --
what's left?
The one that I can --
well, one way is
columns times rows.
What happens if I multiply --
So this was row times
column, it gave a number.
Okay.
Now I want to ask you
about column times row.
If I multiply a column
of A times a row of B,
what shape I ending up with?
So if I take a
column times a row,
that's definitely different from
taking a row times a column.
So a column of A was -- what's
the shape of a column of A?
m by one.
A column of A is a column.
It's got m entries
and one column.
And what's a row of B?
It's got one row and P columns.
So what's the shape -- what do
I get if I multiply a column
by a row?
I get a big matrix.
I get a full-sized matrix.
If I multiply a
column by a row --
should we just do one?
Let me take the column two three
four times the row one six.
That product there --
I mean, when I'm just
following the rules of matrix
multiplication, those rules
are just looking like --
kind of petite, kind of
small, because the rows here
are so short and the
columns there are so short,
but they're the same
length, one entry.
So what's the answer?
What's the answer if I do two
three four times one six, just
for practice?
Well, what's the first
row of the answer?
Two twelve.
And the second row of the
answer is three eighteen.
And the third row of the
answer is four twenty four.
That's a very special
matrix, there.
Very special matrix.
What can you tell me
about its columns,
the columns of that matrix?
They're multiples
of this guy, right?
They're multiples of that one.
Which follows our rule.
We said that the columns of
the answer were combinations,
but there's only -- to take
a combination of one guy,
it's just a multiple.
The rows of the
answer, what can you
tell me about those three rows?
They're all multiples
of this row.
They're all multiples of
one six, as we expected.
But I'm getting a
full-sized matrix.
And now, just to complete
this thought, if I have --
let me write down
the fourth way.
A B is a sum of columns
of A times rows of B.
So that, for example, if my
matrix was two three four
and then had another column,
say, seven eight nine,
and my matrix here has -- say,
started with one six and then
had another column
like zero zero, then --
here's the fourth way, okay?
I've got two columns there,
I've got two rows there.
So the beautiful rule is --
see, the whole thing
by columns and rows
is that I can take the first
column times the first row
and add the second column
times the second row.
So that's the fourth way --
that I can take
columns times rows,
first column times first row,
second column times second
row and add.
Actually, what will I get?
What will the answer be for
that matrix multiplication?
Well, this one it's just
going to give us zero,
so in fact I'm back to
this -- that's the answer,
for that matrix multiplication.
I'm happy to put up here
these facts about matrix
multiplication, because it
gives me a chance to write down
special matrices like this.
This is a special matrix.
All those rows lie
on the same line.
All those rows lie on
the line through one six.
If I draw a picture of
all these row vectors,
they're all the same direction.
If I draw a picture of
these two column vectors,
they're in the same direction.
Later, I would
use this language.
Not too much later, either.
I would say the
row space, which is
like all the
combinations of the rows,
is just a line for this matrix.
The row space is the line
through the vector one six.
All the rows lie on that line.
And the column space
is also a line.
All the columns lie on the
line through the vector two
three four.
So this is like a
really minimal matrix.
And it's because of these ones.
Okay.
So that's a third way.
Now I want to say one more thing
about matrix multiplication
while we're on the subject.
And it's this.
You could also multiply --
You could also cut
the matrix into blocks
and do the
multiplication by blocks.
Yet that's actually so, useful
that I want to mention it.
Block multiplication.
So I could take my matrix A
and I could chop it up, like,
maybe just for simplicity,
let me chop it into two --
into four square blocks.
Suppose it's square.
Let's just take a nice case.
And B, suppose it's
square also, same size.
So these sizes don't
have to be the same.
What they have to do
is match properly.
Here they certainly will match.
So here's the rule for
block multiplication,
that if this has
blocks like, A --
so maybe A1, A2, A3,
A4 are the blocks here,
and these blocks
are B1, B2,3 and B4?
Then the answer
I can find block.
And if you tell me
what's in that block,
then I'm going to be quiet
about matrix multiplication
for the rest of the day.
What goes into that block?
You see, these might be
-- this matrix might be --
these matrices might be, like,
twenty by twenty with blocks
that are ten by ten, to take the
easy case where all the blocks
are the same shape.
And the point is that I could
multiply those by blocks.
And what goes in here?
What's that block in the answer?
A1 B1, that's a
matrix times a matrix,
it's the right size, ten by ten.
Any more?
Plus, what else goes in there?
A2 B3, right?
It's just like block
rows times block columns.
Nobody, I think, not even
Gauss could see instantly
that it works.
But somehow, if we check
it through, all five ways
we're doing the same
multiplications.
So this familiar
multiplication is
what we're really
doing when we do it
by columns, by rows by columns
times rows and by blocks.
Okay.
I just have to, like,
get the rules straight
for matrix multiplication.
Okay.
All right, I'm ready
for the second topic,
which is inverses.
Okay.
Ready for inverses.
And let me do it for
square matrices first.
Okay.
So I've got a square matrix A.
And it may or may not
have an inverse, right?
Not all matrices have inverses.
In fact, that's the most
important question you can ask
about the matrix, is if it's
-- if you know it's square,
is it invertible or not?
If it is invertible, then
there is some other matrix,
shall I call it A inverse?
And what's the -- if
A inverse exists --
there's a big "if" here.
If this matrix exists, and it'll
be really central to figure out
when does it exist?
And then if it does exist,
how would you find it?
But what's the equation
here that I haven't --
that I have to finish now?
This matrix, if it exists
multiplies A and produces,
I think, the identity.
But a real --
an inverse for a square matrix
could be on the right as well
--
this is true, too, that it's --
if I have a -- yeah in
fact, this is not --
this is probably the --
this is something that's not
easy to prove, but it works.
That a left --
square matrices, a left
inverse is also a right
inverse.
If I can find a matrix on the
left that gets the identity,
then also that
matrix on the right
will produce that identity.
For rectangular matrices,
we'll see a left inverse
that isn't a right inverse.
In fact, the shapes
wouldn't allow it.
But for square
matrices, the shapes
allow it and it happens,
if A has an inverse.
Okay, so give me some cases --
let's see.
I hate to be negative
here, but let's talk
about the case with no inverse.
So -- these matrices are called
invertible or non-singular --
those are the good ones.
And we want to be able
to identify how --
if we're given a matrix,
has it got an inverse?
Can I talk about
the singular case?
No inverse.
All right.
Best to start with an example.
Tell me an example -- let's
get an example up here.
Let's make it two by two --
of a matrix that has
not got an inverse.
And let's see why.
Let me write one up.
No inverse.
Let's see why.
Let me write up --
one three two six.
Why does that matrix
have no inverse?
You could answer
that various ways.
Give me one reason.
Well, you could -- if you
know about determinants,
which you're not supposed to,
you could take its determinant
and you would get --
Zero.
Okay.
Now -- all right.
Let me ask you other reasons.
I mean, as for other
reasons that that matrix
isn't invertible.
Here, I could use
what I'm saying here.
Suppose A times other
matrix gave the identity.
Why is that not possible?
Because -- oh, yeah --
I'm thinking about columns here.
If I multiply this matrix A by
some other matrix, then the --
the result -- what can you
tell me about the columns?
They're all multiples
of those columns, right?
If I multiply A by
another matrix that --
the product has columns that
come from those columns.
So can I get the
identity matrix?
No way.
The columns of the identity
matrix, like one zero --
it's not a combination
of those columns,
because those two
columns lie on the --
both lie on the same line.
Every combination is just
going to be on that line
and I can't get one zero.
So, do you see that sort of
column picture of the matrix
not being invertible.
In fact, here's another reason.
This is even a more
important reason.
Well, how can I
say more important?
All those are important.
This is another way to see it.
A matrix has no inverse --
yeah -- here -- now
this is important.
A matrix has no -- a square
matrix won't have an inverse
if there's no inverse
because I can solve --
I can find an X of -- a
vector X with A times --
this A times X giving zero.
This is the reason I like best.
That matrix won't
have an inverse.
Can you -- well, let
me change I to U.
So tell me a vector X that,
solves A X equals zero.
I mean, this is, like,
the key equation.
In mathematics, all
the key equations
have zero on the
right-hand side.
So what's the X?
Tell me an X here --
so now I'm going to put --
slip in the X that you tell me
and I'm going to get zero.
What X would do that job?
Three and negative one?
Is that the one you
picked, or -- yeah.
Or another -- well, if
you picked zero with zero,
I'm not so excited, right?
Because that would always work.
So it's really the
fact that this vector
isn't zero that's important.
It's a non-zero vector and
three negative one would do it.
That just says three of this
column minus one of that column
is the zero column.
Okay.
So now I know that A
couldn't be invertible.
But what's the reasoning?
If A X is zero, suppose I
multiplied by A inverse.
Yeah, well here's the reason.
Here -- this is why this
spells disaster for an inverse.
The matrix can't have an
inverse if some combination
of the columns gives
z- it gives nothing.
Because, I could
take A X equals zero,
I could multiply by A inverse
and what would I discover?
Suppose I take that equation
and I multiply by --
if A inverse existed, which
of course I'm going to come
to the conclusion it can't
because if it existed,
if there was an A inverse
to this dopey matrix,
I would multiply that equation
by that inverse and I would
discover X is zero.
If I multiply A by A inverse
on the left, I get X.
If I multiply by A inverse
on the right, I get zero.
So I would discover X was zero.
But it -- X is not zero.
X -- this guy wasn't zero.
There it is.
It's three minus one.
So, conclusion -- only, it takes
us some time to really work
with that conclusion --
our conclusion will be that
non-invertible matrices,
singular matrices, some
combinations of their columns
gives the zero column.
They they take some
vector X into zero.
And there's no way A
inverse can recover, right?
That's what this equation says.
This equation says I take
this vector X and multiplying
by A gives zero.
But then when I
multiply by A inverse,
I can never escape from zero.
So there couldn't
be an A inverse.
Where here -- okay, now fix --
all right.
Now let me take -- all right,
back to the positive side.
Let's take a matrix that
does have an inverse.
And why not invert it?
Okay.
Can I -- so let me take on
this third board a matrix --
shall I fix that up a little?
Tell me a matrix that
has got an inverse.
Well, let me say one three
two -- what shall I put there?
Well, don't put six,
I guess is -- right?
Do I any favorites here?
One?
Or eight?
I don't care.
What, seven?
Seven.
Okay.
Seven is a lucky number.
All right, seven, okay.
Okay.
So -- now what's our idea?
We believe that this
matrix is invertible.
Those who like determinants have
quickly taken its determinant
and found it wasn't zero.
Those who like columns,
and probably that --
that department is not
totally popular yet --
but those who like columns
will look at those two columns
and say, hey, they point
in different directions.
So I can get anything.
Now, let me see, what do I mean?
How I going to
computer A inverse?
So A inverse --
here's A inverse, now,
and I have to find it.
And what do I get when I
do this multiplication?
The identity.
You know, forgive me for
taking two by two-s, but --
lt's good to keep the
computations manageable and let
the ideas come out.
Okay, now what's
the idea I want?
I'm looking for this
matrix A inverse, how
I going to find it?
Right now, I've got
four numbers to find.
I'm going to look
at the first column.
Let me take this
first column, A B.
What's up there?
What -- tell me this.
What equation does the
first column satisfy?
The first column satisfies A
times that column is one zero.
The first column of the answer.
And the second column,
C D, satisfies A times
that second column is zero one.
You see that finding the inverse
is like solving two systems.
One system, when the
right-hand side is one zero --
I'm just going to split
it into two pieces.
I don't even need to rewrite it.
I can take A times --
so let me put it here.
A times column j of A inverse
is column j of the identity.
I've got n equations.
I've got, well,
two in this case.
And they have the
same matrix, A,
but they have different
right-hand sides.
The right-hand sides
are just the columns
of the identity, this
guy and this guy.
And these are the two solutions.
Do you see what I'm going --
I'm looking at that
equation by columns.
I'm looking at A
times this column,
giving that guy, and A times
that column giving that guy.
So -- Essentially -- so
this is like the Gauss --
we're back to Gauss.
We're back to solving systems of
equations, but we're solving --
we've got two right-hand
sides instead of one.
That's where Jordan comes in.
So at the very beginning
of the lecture,
I mentioned Gauss-Jordan,
let me write it up again.
Okay.
Here's the Gauss-Jordan idea.
Gauss-Jordan solve
two equations at once.
Okay.
Let me show you how
the mechanics go.
How do I solve a
single equation?
So the two equations
are one three two seven,
multiplying A B gives one zero.
And the other
equation is the same
one three two seven
multiplying C D gives zero one.
Okay.
That'll tell me the two
columns of the inverse.
I'll have inverse.
In other words, if I can
solve with this matrix A,
if I can solve with
that right-hand side
and that right-hand
side, I'm invertible.
I've got it.
Okay.
And Jordan sort of said to
Gauss, solve them together,
look at the matrix -- if
we just solve this one,
I would look at one
three two seven,
and how do I deal with
the right-hand side?
I stick it on as an
extra column, right?
That's this augmented matrix.
That's the matrix when I'm
watching the right-hand side
at the same time, doing the
same thing to the right side
that I do to the left?
So I just carry it along
as an extra column.
Now I'm going to carry
along two extra columns.
And I'm going to do
whatever Gauss wants, right?
I'm going to do elimination.
I'm going to get
this to be simple
and this thing will
turn into the inverse.
This is what's coming.
I'm going to do elimination
steps to make this
into the identity,
and lo and behold,
the inverse will show up here.
K--- let's do it.
Okay.
So what are the
elimination steps?
So you see -- here's my matrix
A and here's the identity, like,
stuck on, augmented on.
STUDENT: I'm sorry...
STRANG: Yeah?
STUDENT: -- is the two and the
three supposed to be switched?
STRANG: Did I -- oh, no,
they weren't supposed to be
switched.
Sorry.
Thanks.
Okay.
Thank you very much.
And there -- I've
got them right.
Okay, thanks.
Okay.
So let's do elimination.
All right, it's going
to be simple, right?
So I take two of this
row away from this row.
So this row stays
the same and two
of those come away from this.
That leaves me with a zero and
a one and two of these away from
this is that what
you're getting --
after one elimination step --
Let me sort of separate the --
the left half from
the right half.
So two of that first row got
subtracted from the second row.
Now this is an upper
triangular form.
Gauss would quit, but
Jordan says keeps going.
Use elimination upwards.
Subtract a multiple of
equation two from equation one
to get rid of the three.
So let's go the whole way.
So now I'm going to -- this guy
is fine, but I'm going to --
what do I do now?
What's my final step that
produces the inverse?
I multiply this by
the right number
to get up to ther to
remove that three.
So I guess, I --
since this is a one,
there's the pivot sitting there.
I multiply it by three
and subtract from that,
so what do I get?
I'll have one zero -- oh,
yeah that was my whole point.
I'll multiply this by three
and subtract from that,
which will give me seven.
And I multiply this by three
and subtract from that,
which gives me a minus three.
And what's my hope, belief?
Here I started with
A and the identity,
and I ended up with
the identity and who?
That better be A inverse.
That's the Gauss Jordan idea.
Start with this long matrix,
double-length A I, eliminate,
eliminate until this
part is down to I,
then this one will --
must be for some reason,
and we've got to find the
reason -- must be A inverse.
Shall I just check
that it works?
Let me just check that -- can I
multiply this matrix this part
times A, I'll carry A
over here and just do that
multiplication.
You'll see I'll do it
the old fashioned way.
Seven minus six is a one.
Twenty one minus
twenty one is a zero,
minus two plus two is a zero,
minus six plus seven is a one.
Check.
So that is the inverse.
That's the Gauss-Jordan idea.
So, you'll -- one of the
homework problems or more than
one for Wednesday will ask
you to go through those steps.
I think you just got to go
through Gauss-Jordan a couple
of times, but I --
yeah -- just to
see the mechanics.
But the, important
thing is, why --
is, like, what happened?
Why did we -- why did
we get A inverse there?
Let me ask you that.
We got -- so we take --
We do row reduction, we do
elimination on this long matrix
A I until the first half
Then a second half
is A inverse. is up.
Well, how do I see that?
Let me put up here
how I see that.
So here's my Gauss-Jordan thing,
and I'm doing stuff to it.
So I'm -- well,
whole lot of E's.
Remember those are those
elimination matrices.
Those are the -- those are the
things that we figured out last
time.
Yes, that's what an elimination
step is it's in matrix form,
I'm multiplying by some Es.
And the result -- well, so I'm
multiplying by a whole bunch
of Es.
So, I get a --
can I call the overall matrix E?
That's the elimination matrix,
the product of all those little
pieces.
What do I mean by little pieces?
Well, there was an
elimination matrix
that subtracted two of
that away from that.
Then there was an
elimination matrix
that subtracted three
of that away from that.
I guess in this
case, that was all.
So there were just two
Es in this case, one
that did this step and
one that did this step
and together they gave me
an E that does both steps.
And the net result
was to get an I here.
And you can tell me
what that has to be.
This is, like, the
picture of what happened.
If E multiplied A,
whatever that E is --
we never figured
it out in this way.
But whatever that E times
that E is, E times A is --
What's E times A?
It's I.
That E, whatever the heck it
was, multiplied A and produced
So E must be --
E A equaling I tells us what
E is, I. namely it is --
STUDENT: It's the inverse of A.
STRANG: It's the inverse of A.
Great.
And therefore, when the second
half, when E multiplies I,
it's E --
Put this A inverse.
You see the picture
looking that way?
E times A is the identity.
It tells us what E has to be.
It has to be the
inverse, and therefore,
on the right-hand
side, where E --
where we just smartly
tucked on the identity,
it's turning in, step by step --
It's turning into A inverse.
There is the statement of
Gauss-Jordan elimination.
That's how you find the inverse.
Where we can look at
it as elimination,
as solving n equations
at the same time -- --
and tacking on n columns,
solving those equations and up
goes the n columns of
A inverse Okay, thanks.
See you on Wednesday.
