- [Instructor] So let's say you're running

some kind of company and you guys produce widgets.

You produce some little trinket that people enjoy buying

and the main costs that you have are labor,

the workers that you have creating these and steel.

And let's just say that your labor costs

are $20 per hour, $20 each hour

and then your steel costs are $2,000.

Keep the numbers kind of related to each other.

$2,000 for every ton of steel.

And then you've had your analysts work a little bit

on trying to model the revenues you can make

with your widgets as a function of

hours of labor and tons of steel.

Now let's say the revenue model that they've come up with.

The revenue as a function of hours of labor

and then S for steel, let's say,

is equal to about 100 times the hours of labor

to the power of 2/3 multiplied by

the tons of steel to the power of 1/3.

If you put in a given amount of labor

and a given amount of steel, this is about how much money

you're gonna expect to earn.

And of course you wanna earn as much as you can

plus you actually have a budget for how much

you're able to spend on all these things.

And your budget is $20,000.

You're willing to spend $20,000

and you wanna make as much money as you can,

according to this model based on that.

Now this is exactly the kind of problem

that the Lagrange multiplier technique is made for.

We're trying to maximize some kind of function

and we have a constraint.

Now right now the constraint isn't written as a formula,

but we can pretty easily write as a formula.

Because what makes up our budget?

Well, it's gonna be the number of

hours of labor multiplied by 20,

so that's gonna be $20 per hour

multiplied by the number of hours you put in

plus $2000 per tons of steel times

the tons of steel that you put in.

So the constraint is basically that

you have to have these guys equal $20,000.

I mean you could say less than,

you could say you're not willing to go any more than that,

but intuitively and in reality,

it's gonna be the case that in order

to maximize your revenues, you should squeeze every dollar

that you have available and actually hit this constraint.

So this right here is the constraint of our problem.

And let's go ahead and give this guy a name,

the function that we're dealing with a name.

And I'm gonna call it g of h, s, which is gonna be that guy.

And now if you'll remember in the last few videos,

the way we visualize something like this,

is to think about the set of all possible input,

so in this case, you might be thinking about the h s plane,

the number of hours of labor on one axis,

the number of tons of steel on another.

And this constraint, well, in this case,

it's a linear function, so this constraint is gonna give us

some kind of line that tells us which pairs of s and h

are gonna achieve that constraint.

And then the revenue function that

we're dealing with will have certain contours.

Maybe revenues of $10,000 have

a certain contour that looks like this

and revenues of $100,000 have a certain contour

that looks like this.

But what we want is to find which value

is barely touching the constraint curve.

Just tangent to it at a given point

cause that's gonna be the contour line,

where if you up the value by just a little bit,

it would no longer intersect with that curve,

there would no longer be values

of h and s that satisfy this constraint.

And the way to think about finding that tangency

is to consider the vector perpendicular

to the tangent line to the curve at that point,

which fortunately is represented by... (mumbles)

Make some room for myself here.

Represented by the gradient, the gradient of our r function,

the function whose contour's this is, the revenue.

And what it means for this to be

tangent to the constraint line

is that there's gonna be another vector,

the gradient of g, of our constraint function,

that points in the same direction,

that's proportional to that.

And typically the way you write this

is to say that the gradient of this function

is proportional to the gradient of g

and this proportionality constant

is called our Lagrange multiplier.

It's called the Lagrange multiplier.

So, let's go ahead and start working it out.

Let's first compute the gradient of r.

So the gradient of r is gonna be the partial derivative of r

with respect to its first variable, which is h,

so partial derivative with respect to h.

And the second component is its partial derivative

with respect to that second variable, s, with respect to s.

And in this case, that first partial derivative,

if we treat h as a variable and s as a constant,

then that 2/3 gets brought down,

so that'll be 100 times 2/3 times h to the power of,

well, we've gotta subtract one from 2/3

when we bring it down, so that'll be negative 1/3

multiplied by s to the 1/3.

And then the second component here,

the partial derivative with respect to s,

is gonna be 100 times, well now,

by treating s as the variable,

we take down that 1/3, so that's 1/3,

h to the 2/3 just looks like a constant

as far as s is concerned and then we take s

to the 1/3 minus 1, which is negative 2/3.

Great, so that's the gradient of R.

And now we need the gradient of g.

And that one's a lot easier, actually,

cause g is just a linear function.

So when we take the gradient of g,

which is its partial derivative,

with respect to h, partial h, and its partial derivative

with respect to s, partial s.

Well, the partial with respect to h is just 20.

The function looks like 20 times h plus something

that's a constant so that ends up being 20.

And then the partial with respect to s,

likewise is just 2,000, cause it's just some constant

multiplied by s plus a bunch of other stuff

that looks like constants.

So that's great, and this means

when we set the gradient of r equal to the gradient of g,

the pair of equations that we get,

here let me just write it all out again,

is we have this top one, which I'll call 200/3 times,

and let's go ahead and do a little simplifying

while I'm rewriting things here.

So h to the 1/3 is really one over, h to the negative 1/3,

sorry, is one over h to the 1/3, that's s to the 1/3.

So all of this, that first component,

is being set equal to the first component

of the gradient of g, which is 20

times lambda, times this Lagrange multiplier,

cause we're not setting the gradients equal to each other,

we're just setting them proportional to each other.

So that's the first equation and then the second one,

I'll go ahead and do some simplifying

while I rewrite that one also.

That's gonna be 100/3 and then h to the 2/3

so times h to the 2/3 divided by s to the 2/3

cause s to the negative 2/3 is the same

as 1 over s to the 2/3.

All of that is equal to 2,000 times lambda.

And the important thing it's that same lambda,

because the entire vector has to be proportional.

And I think right here's probably a pretty good point

to stop and in the next video,

I'll go ahead and work through the details

and we'll land on a solution.