- [Voiceover] Hello everyone.

So here, I'd like to talk about harmonic functions.

Now, harmonic functions

are a very special kind

of multivariable function,

and they're defined in terms of the Laplacian,

which I've been talking about

in the last few videos.

So the Laplacian, which we denote

with this upper right-side-up triangle,

is an operator that you might take

on a multivariable function.

So it might have two inputs,

it could have, you know, a hundred inputs,

just some kind of multivariable function

with a scalar output.

And I talked about it in the last few videos,

but as a reminder,

it's defined as the divergence

of the gradient of F,

and it's kind of like the second derivative.

It's sort of the way to extend the idea

of the second derivative

into multiple dimensions.

Now, what a harmonic function is,

is one where the Laplacian is equal to zero.

And it's equal to zero

at every possible input point.

And sometimes the way that people

write this to distinguish it,

they'll make a kind of triple equals sign,

maybe saying like, "Equivalent to zero."

And this is really just a way of emphasizing

that it's equal to zero

at all possible input points.

It's, you know, not an equation

that you're solving for the specific X and Y,

where it equals zero.

It's a statement about the function.

And to get our head around this,

because it's kind of a,

you know, as you're just starting

to learn about the Laplacian,

it's hard to just immediately

see the intuition for what this means,

let's think about what it means

for a single variable function.

If you just have some single variable function of X,

and you're looking at its second derivative,

which is kind of the analog of the Laplacian,

what does it mean if that's equal to zero?

Well, we can integrate it,

we can take the antiderivative,

and say, that means that the single derivative of F,

well let's see, what functions

have a derivative that's zero?

The only functions are the constant ones,

so C is just gonna mean some constant here.

And if you integrate that again,

say what function has,

as its derivative, a constant,

well, it's gonna be that constant times X,

plus some other constant,

some other constant K.

So basically, linear functions.

So if you're thinking of a graph,

it's just something that's got a line,

line passing through it like that.

And this should kind of make sense

if you think of the geometric interpretation

for the second derivative.

Because if you're just looking at a random,

you know, arbitrary function

that's kind of curving as it does,

the second derivative is negative

when this concavity is down.

So this right here would be a point

where the second derivative,

it's not zero, it's negative.

And over here, when the concavity is up,

and it's got a sort of bowl shape,

that's where the second derivative is positive.

So if we're saying that the second derivative

has to always be zero,

then it can't curve down and it can't curve up,

and it can't do that anywhere,

so basically there's no curving allowed,

so whatever direction it starts at,

it's not allowed to curve,

so it just sticks straight like that.

But once we extend this to the idea

of a multivariable function,

things can get a lot more interesting

than just a straight line.

So an example,

I've got the graph here

of a multivariable function

that happens to be harmonic.

So the graph that you're looking at,

this is of a two variable function,

and the function specifically is

F of XY,

XY,

is equal to E to the X

multiplied by

sine of Y.

And as we're looking at the graph here,

hopefully it makes a little bit of sense

why this is sort of an E to the X

sine of Y pattern.

'Cause as we're moving in the positive X direction,

this here is the positive X direction,

you have this exponential shape,

and this corresponds with the fact that over here

we've got an E to the X,

so as you move X,

it kinda looks like E to the X,

and it's being multiplied by something

that is a function of Y.

So if you're holding Y constant,

this just looks like a constant.

But notice, if that was a,

if that was a negative constant,

if sine of Y at some point

happens to be negative,

then your whole exponential function

actually kind of goes down.

It's sort of like a negative E to the X look.

But if you imagine moving in the Y direction,

so instead of the pure X direction like that,

if we imagine ourselves moving

with the input going along,

let's see what it would be,

it would be this way,

positive Y direction,

you have this sort of sinusoidal shape,

and that should make sense

because you've got this sine of Y.

And depending on what E to the X is,

the amplitude of that sine wave

is gonna get, you know,

really high at some points here.

It's going way up and way down.

But if E to the X was really small, you know,

it hardly, hardly even looks

like it's wiggling over here.

It pretty much looks flat.

So that's the graph that we're looking at.

And I'm telling you right now,

I claim that this is harmonic.

This is a function whose Laplacian

is equal to zero.

And what that would mean is that

as we go over here and we say,

we evaluate the Laplacian of F,

which, just to remind you,

there's a different formula

rather than thinking divergence of gradient,

that turns out to be completely the same

as saying, you take the second derivative

of that function with respect to X,

that's its first input,

and you add that,

let's see, second derivative with respect to X,

you add that to the second derivative

of your function

with respect to the next variable.

And you keep doing this

for all of the different variables that there are,

but this is just a two variable function,

so you do this twice.

The claim is that this

is always equal to zero.

So I might say, kind of equivalent,

at every possible input,

it's equal to zero.

And I think I'll leave that

as something for you to compute.

It might be kind of good practice

to kind of get a feel for

computing the Laplacian.

But what I wanna do is interpret

what does this actually mean, right?

'Cause you can plug it through,

and you can see, ah yes,

at all possible inputs, it will be zero.

But what does that mean?

Because in the single variable context,

once we started thinking about

the geometric interpretation

of a second derivative as this concavity,

it sort of made sense

that forcing it to be zero

will give us a straight line.

But clearly, that's not the case.

This is much more complicated

than a straight line.

And for that,

I want to give a kind of a different

way that you can think about

the single variable second derivative.

On the one hand, you can think of,

let's say, negative second derivative,

as being this concavity

where it's kind of frowning down.

But another way you can maybe think about this

is saying that all of the neighbors of your point,

if you go a little bit to the left, right,

you've got an input point here,

and if you go a little bit to the left,

the neighbor is less than it,

and if you go a little bit to the right,

that other neighbor is also less than it.

So it's kind of a way of saying,

hey, if you look at the neighbors of your input,

so if you, if you happen to be making the claim

that F double prime, at some particular input,

like X sub O,

is less than zero,

it's saying that all of the neighbors

of X sub O,

all of the neighbors of that point,

are less than it.

And if you do a similar thing

over at a positive concavity point

where it's kind of smiling up,

you say, well its neighbor to the right

has a greater value,

and its neighbor to the left has a greater value.

So at some point, where the second derivative,

instead of being less than zero,

happens to be,

happens to be greater than zero,

that means that the neighbors

tend to be greater than the point itself.

And even if you're looking at a circumstance

that isn't this idealized, you know,

it happens to be a local minimum,

but let's say you're looking at a graph.

Let's say you're looking at a function

at a point where it's concave up, right?

It's concave up, but it's not this idealized,

local minimum kind of circumstance.

So instead, you might be looking

at a point like this,

and if you look at, you know,

its neighbor to the left,

that'll have some value that's actually less

than your original guy,

so the neighbor looks like

it's less than it on the left,

but if you move that same distance to the right,

its neighbor is greater.

But you would say, on average,

if you took the average value of the neighbors,

the neighbor on the right kind of outbalances

the neighbor on the left,

and you would say, on average,

its neighbors are greater than the point itself.

So let's say that input point there

was like X sub O,

that would mean that the second derivative

of your function at that point,

you know, is greater than zero.

So this positive concavity,

you can also think of it

as a measure of, on average,

are the neighbors greater than your original point

or less than it?

And the reason I'm saying this

is because this idea,

of kind of comparing your neighbors

to the original point

is a much better way to contemplate

the Laplacian in the multivariable world.

So if we look at a function like this,

and let's say we're looking at it

kind of from a bird's eye view.

So we've got our XY plane, right?

This over here is the X-axis,

and this up here is the Y-axis.

And let's say that we're looking

at some specific input point.

With the Laplacian,

you wanna start thinking about

a circle of points around it,

all of its neighbors,

and in fact, think of a perfect circle,

so all of the points

that are a specified distance away.

The question the Laplacian is asking is,

"Hey, are those neighbor points, on average,

"greater than or less than your original point?"

And this is actually,

this is how I introduced the Laplacian

in the original video where I was giving

kind of the intuition for the Laplacian,

you're asking, "Do the points around a given input

"happen to be greater than it or less than it?"

And if you're looking at a point

where the Laplacian of your function

happens to be greater than zero at some point,

that would mean all of the neighbors

tend to be, on average, greater than your point.

Whereas if you're looking at a point

where the Laplacian of your function

is less than zero,

then all of those neighbors, on average,

would be less than your point.

So in particular, you know,

if the Laplacian was less than zero,

your point might look like a local maximum.

Or if the Laplacian was greater than zero,

it might look like a local minimum,

'cause all of its neighbors

would be greater than where it is.

But for harmonic functions,

what makes them so special

is that you're saying

the value of the function itself,

or the value of the Laplacian of the function

at every possible point

is equal to zero.

So no matter what point you choose,

those neighbors are gonna be,

on average, the same value as this guy.

So the height of the graph above those neighbors

will, on average, be the same.

So,

so if we kind of look around the graph,

what that should mean is,

let's say you're looking at an input point,

you know, the output of this guy.

And if we looked at all of its,

the circle of its neighbors,

and kind of projected them up onto the graph,

what it should mean is that the height

of all the points on this circle,

on average, are the same as that.

And no matter where you look,

that should kind of average out.

And again, I encourage you to

take a look at,

take a look at this function

and actually evaluate the Laplacian

to see that it's zero.

But what's interesting is it's not at all clear,

just looking at this E to the X

times sine of Y formula,

that the average value of a circle of input points

is always gonna kind of equal the value

of the point at the center.

That's not something you can easily tell

just looking at that formula.

But with what's not that hard a computation,

you can make this conclusion,

which is pretty far-reaching.

And this comes up all the time in physics.

You know, for example, heat is one

where maybe you wanna describe

how the heat at a certain point in a room

is related to the average value of the heat

of all of the points kind of around it.

And in fact, it comes up in all sorts of circumstances

where you have some point in physical space

and something about that point,

maybe like the rate at which

some property of it is changing

corresponds to the average value

at points around it.

So whenever you're sort of relating neighbors

to your original point, the Laplacian comes in,

and harmonic functions have this tendency

to correspond to some notion of stability.

And I won't go deeper into that now.

This is really,

that really starts to get into the topic

of partial differential equations.

But at least in the context of

just multivariable calculus,

I wanted to shed some light

on interpreting this operator,

and kind of interpreting

the physical and geometric properties

that that implies about a function.

And with that,

I will see you next video.