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- [Voiceover] Hello hello again.
So, in the last video,
I started talking about how you interpret
the partial derivative of a parametric surface function,
of a function that has a two variable input,
and a three variable vector-valued output.
And we typically visualize those as a surface
in three dimensional space,
and the whole process, I was saying, you think
about how a portion of the t-s plane
moves to that corresponding output.
And again, I'm kind of cheating with this animation,
where, really, this isn't the t-s plane, right?
This is on the x-y plane.
t-s plane should just be some separate space over here,
and we're imagining moving that separate space
over into three dimensions.
But that's harder to animate, so I'm just not gonna do it,
and I'm gonna instead keep things inside the x-y plane here.
We're thinking about the squares
being t and s ranging each from 0 to 3.
And, what I said, for partial derivative with respect to t,
is you imagine the line
that represents movement in the t direction.
You see how that line gets mapped
as all of the points move to their corresponding output.
And the partial derivative vector
gives you a certain tangent vector
to the curve representing that line,
which corresponds to movement in the t direction.
And the longer that is, the faster the movement,
The more sensitive it is to nudges in the t direction.
In the s direction, let's say we were to take
the partial derivative with respect to s.
So I'll kind of clear this up here.
Also clear up this guy.
And if you said instead,
"what if we were doing it with respect to s," right?
Partial derivative of v,
the vector-valued function, with respect to s.
Well, you do something very similar.
You would say "OK, What is the line that corresponds
"to movement in the s direction?"
And the way I've drawn it,
it's always going to be perpendicular,
because we're in the t-s plane,
the t axis is perpendicular to the s plane.
And in this case, this line represents t = 1, right?
You're saying t constantly equals 1,
but you're letting s vary.
And if you see how that line maps,
as you move everything from the input space
over to the corresponding points in the output space,
that line tells you what happens
as you're varying the s value of the input.
It kind of starts curving this way,
and then it curves very much up and
kind of goes off into the distance there.
And again, the grid lines here really help,
because every time that you see the grid lines intersect,
one of the lines represents movement in the t direction,
and the other represents movement in the s direction.
And for partial derivatives, we think very similarly.
You think of that partial s as representing-
(voiced zooming sounds)
Zoom on back here.
That partial s you think of as representing
a tiny movement in the s direction,
just a little smidge and nudge,
somehow nudging that guy along.
And then the corresponding nudge
you look for in the output space,
you say okay, if we nudge the input that much,
and we go over to the output, and...
and maybe that tiny nudge
corresponded with one that's three times bigger.
I don't know, but it looked like it stretched things out.
So that tiny nudge might turn into something
that's still quite small,
but maybe three times bigger.
But it's a vector.
What you do is, you think of that vector
as being your partial v,
and you scale it by whatever the size
of that partial s was, right?
So the result that you get
is a tangent vector that's not puny, not a tiny nudge,
but is actually a sizable tangent vector.
And it's going to correspond to the rate
at which changes, not just tiny changes,
but the rate at which changes in s
cause movement in the output space.
So, let's actually compute it for this case,
just get some good practice computing things.
And if we look up here, the t value
which used to be considered a variable
when we were doing it with respect to t.
But now that t value looks like a constant,
so its derivative is zero.
Then -s², with respect to s,
has a derivative of -2s.
st: s looks like a variable, t looks like a constant,
the derivative is just that constant, t.
Down here, ts²: t looks like a constant,
s looks like a variable, so...
2 times t times s.
And then over here, we're subtracting off,
s is the variable, t² looks like a constant,
so that constant.
And let's say we plug in the value (1,1).
This red dot corresponds to (1,1),
so what we would get here,
s is equal to 1, so that's -2,
t is equal to 1, so that's 1,
then 2 times 1 times 1, I'll write it.
2*1*1
minus 1²
is gonna correspond to 1, that's 2-1.
So what we would expect for the tangent vector,
the partial derivative vector,
is the x-component should be negative,
and then the y and z-components should each be positive.
And if we go over, and we take a look
at what the movement along the curve actually is,
that lines up, right?
Because, as you kind of zip along this curve,
you're moving to the left, so the x-component
of the partial derivative should be negative.
But you're moving upwards as far as y is concerned,
and you can also kinda see that the leftward movement
is kind of twice as fast as the upward motion.
The slope favors the x direction.
And then as far as the z component is concerned,
you are, in fact, moving up.
And maybe you could say,
"Well, how do you know which way you're moving,
"are you moving that way, or is everything
"switched the other way around?"
And the benefit of animation here is,
we can say, "Ah, as s is ranging from zero
"up to three, this is the increasing direction."
And you just keep your eye on what that direction is
as we move things about.
And that increasing direction does kind of correspond
with moving along the curve this way.
So, you get a tangent vector in the other way.
And one kinda nice thing about this, then, is
the two different partial derivative vectors that we found
Each one of them, you could say,
is a tangent vector to the surface, right?
So the one that was a partial derivative
with respect to t, over here,
kinda goes in one direction,
and the other one kinda gives you a different notion
of what a tangent vector on the surface could be.
And you could have a notion of directional derivative too,
that kind of combines these in various ways,
and that will get you all the different ways
that you can have a vector tangent to the surface.
And later on, I'll talk about things like tangent planes,
if you want to express what a tangent plane is.
And you kind of think of that as being defined
in terms of two different vectors.
But for now, that's really all you need to know
about partial derivatives of parametric surfaces.
And the next couple videos, I'll talk about
what partial derivatives of vector-valued functions
can mean in other contexts,
because it's not always a parametric surface,
and maybe you're not always thinking
about a curve that could be moved along,
but you still want to think, "How does this input nudge
"correspond to an output nudge,
"and what's the ratio between them."
So with that, I'll see you next video.