Solving the matrix vector equation Video Lecture - Engineering Mathematics

Voiceover:In the last video we saw
that we could take a
system of two equations
with two unknowns and represent it
as a matrix equation where the matrix A's
are the coefficients here
on the left-hand side.
The column vector X has our two
unknown variables, S and T.
Then the column vector B is essentially
representing the
right-hand side over here.
What was interesting about it,
then that would be the equation A,
the matrix A times the column vector X
being equal to the column vector B.
What was interesting about that is we saw
well, look, if A is invertible,
we can multiply both the
left and the right-hand sides
of the equation,
and we have to multiply
them on the left-hand sides
of their respective sides by A inverse
because remember matrix,
when matrix multiplication order matters,
we're multiplying the left-hand side
of both sides of the equation.
If we do that then we
can get to essentially
solving for the unknown column vector.
If we know what column vector X is,
then we know what S and T are.
Then we've essentially solved
this system of equations.
Now let's actually do that.
Let's actually figure
out what A inverse is
and multiply that times
the column vector B
to figure out what the column vector X is,
and what S and T are.
A inverse, A inverse is equal to
one over the determinant of A,
the determinant of A for a two-by-two here
is going to be two times
four minus negative
two times negative five.
It's going to be eight minus positive 10,
eight minus positive 10,
which would be negative two.
This would become negative
two right over here.
Once again, two times four is eight minus
negative two times negative five
so minus positive 10 which
gets us negative two.
You multiply one over the determinant
times what is sometimes
called the adjoint of A
which is essentially swapping the top left
and bottom right or at least
for a two-by-two matrix.
This would be a four.
This would be a two.
Notice I just swapped these,
and making these two negative,
the negative of what they already are.
This is from a negative two this is
going to become a positive two,
and this right over
here is going to become
a positive five.
If all of this looks
completely unfamiliar to you,
you might want to review the tutorial
on inverting matrices
because that's all I'm doing here.
So A inverse is going to be equal to,
A inverse is going to be equal to,
let's see, this is negative 1/2 times four
is negative two.
Negative 1/2, negative 1/2 times five
is negative 2.5, negative 2.5.
And negative 1/2 times
two is negative one.
Negative 1/2 times two is negative one.
So that's A inverse right over here.
Now let's multiply A inverse times
our column vector, seven, negative six.
Let's do that.
This is A inverse. I'll rewrite it.
Negative two, negative 2.5, negative one,
negative one times seven and negative six.
Times, I'll just write
them all in white here now.
Seven, negative six.
We've had a lot of practice
multiplying matrices.
So what is this going to be equal to?
The first entry is
going to be negative two
times seven which is negative 14 plus
negative 2.5 times negative six.
Let's see. That's going to be positive.
That's going to be 12 plus another 3.
That's going to be plus 15.
Plus 15.
Negative 2.5 times negative six
is positive 15.
Then we're going to have negative one
times seven which is negative seven plus
negative one times negative six.
Well, that is positive six.
So the product A inverse B
which is the same things
as a column vector X
is equal to,
we deserve a little
bit of a drum roll now,
the column vector one, negative one.
We have just shown that this is equal to
one, negative one or that X is equal to
one, negative one,
or we could even say
that the column vector,
the column vector ST,
column vector with the
entries S and T is equal to,
is equal to one, negative one,
is equal to one, negative one
which is another way of saying
that S is equal to one
and T is equal to negative one.
I know what you're saying.
I said this in the last video
and I'll say it again in this video.
You're like, "Well, you
know, it was so much easier
"to just solve this system directly
"just with using elimination
or using substitution."
I agree with you, but
this is a useful technique
because when you are doing problems
in computation there may be situations
where you have the left-hand side
of this system stays the same,
but there's many, many,
many different values
for the right-hand side of the system.
So it might be easier to just compute
the inverse once and
just keep multiplying,
keep multiplying this inverse times
the different what we have
on the right-hand side.
You probably are familiar with some types,
you have graphics processors,
and graphics cards on computers
and they talk about
special graphic processors.
What these are really all about
are the hardware that is special-purposed
for really fast matrix multiplication
because when you're
doing graphics processing
when you're thinking about modeling things
in three dimensions,
and you're doing all
these transformations,
you're really just doing a lot of
matrix multiplications
really, really, really fast
in real time so that to
the user playing the game
or whatever they're doing,
it feels like they're in some type
of a 3D, real-time reality.
Anyway, I just want to point that out.
This wouldn't be, if I
saw this just randomly
my instincts would be to
solve this with elimination,
but this ability to think of this
as a matrix equation is a
very, very useful concept,
one actually not just in computation,
but also as you go into
higher level sciences
especially physics, you will see a lot of
matrix vector equations like this
that kind of speak in generalities.
It's really important to think about
what these actually represent
and how they can actually be solved.