- [Instructor] So let's dig a little bit more
into matrices and their inverses.
And in particular, I'm gonna explore the situations
in which there might not be an inverse for a matrix.
So just as a review, we think about if we have some matrix,
A, is there some other matrix which we could call A inverse
that when we take the composition of them,
so if we viewed them each as transformations,
we would end up with the identity transformation.
Or if we take the product of the two,
you get the identity matrix.
And we would also think about it, well,
if A inverse undoes A, then A should undo A inverse
to also get the identity matrix.
And so another way to think about it, if I take
some type of region in the coordinate plane,
so this is my x axis, this is my y axis.
And so let's say my original region looks something
like this right over here.
And I apply the transformation A, and I get something
that looks like this, just making up some things.
So if I apply the transformation A,
it takes me from that region to that region.
Then we also have a sense that, okay, A inverse,
if you transformed this purple thing,
it should take you back to where you began.
Because if you start with this little blue thing
and if you have the composition, well, then that should
just be transforming it with the identity transformation,
so you should just get back to this little blue thing here.
Now this might start triggering some thoughts
about determinants, because you might remember
that the determinant of a matrix tells us
how much a region's area will be scaled by.
In particular, let's say that matrix A
takes a region that has an area of, I don't know,
let's call this area b and let's take,
let's say it takes that area to 5 times b.
So the area here is 5b.
Well, we know that that scaling of 5
you can determine from the determinant of matrix A.
That would tell us that the absolute value
of the determinant of matrix A is going to be equal to 5.
But what does that tell us about the absolute value
of the determinant of A inverse then?
Well, if A is scaling up by 5, it scales areas up by 5,
then A inverse must be
scaling areas down by 5.
So the absolute value of the determinant
of A inverse should be 1 over 5.
And so now we have a general property.
I just happened to use the number five here,
but generally speaking, the absolute value
of the determinant of matrix A, if it has an inverse,
should be equal to 1 over the absolute value
of the determinant of A inverse.
And we can of course write that the other way around.
The absolute value of the determinant of A inverse
should be equal to 1 over, or the reciprocal of,
the absolute value of the determinant of A.
The sum comes straight out of this property
that the absolute value of the determinant tells you
how much you scale an area by.
Well knowing that both of these statements need to be true
for any matrix A that has an inverse,
it gives us a clue as to at least one way
to rule out matrices that might not have inverses.
If I were to tell you that the determinant
of matrix A is zero, will that have an inverse?
Well, it can't because if this quantity right over here
is zero, or this quantity right over here is zero,
that would mean that the absolute value of the determinant
of the inverse of the matrix needs to be one over zero,
which is undefined.
And so we have an interesting conclusion here.
If the determinant of a matrix is equal to zero
there is not going to be an inverse,
because let's say that there was some transformation
that determinant was zero, instead of something
that's taking up two-dimensional area to something else
that takes two-dimensional area, it would transform
something that takes up two dimensional area
to something that takes no area.
So maybe a curve like that,
that takes up no area or a line or a point.
And if you transform to say, a line,
how do you transform back?
You'd have to scale up the area infinitely
in order for it to take up some two dimensional space.
So big takeaway.
We've just said, if the determinant of a matrix
is equal to zero, you're not going to find an inverse.
And it actually turns out the case that any other matrix,
you can find an inverse, but I'm not going to prove that
just yet, but hopefully you feel good
about this principle right over here.