[MIT OCW] 18.02 Multivariable Calculus - 13 笔记

MIT OCW

18.02 Multivariable Calculus

Lagrange Multiplier

We've known the method to maximize or minimize a multivariable function, but what happens if there are some constraint? The critical point usually does not fulfill the constraint, so we have to maximize or minimize in another way.

Introduction

e.g. Find the closest point to origin on $xy=3$.
Idea We are going to minimize $f(x,y)=x^2+y^2$ subject to $xy=3$. These are the level surfaces of $f(x,y)$ and $g(x,y)=xy=3$.
multiplier-1.png
When the level surface $f(x,y)=c$ becomes smaller and smaller, until it has no intersection with the $xy=3$, then we have almost achieved the goal. We can find that at the maximum or minimum $f_0$, the level surface $f(x,y)=f_0$ is tangent to the level surface $g(x,y)=3$.
multiplier-2.png
It means that the gradient at $f(x,y)$ is parallel to $g(x,y)$, namely, $\nabla f=\lambda\nabla g$, where $\lambda$ is an unknown. So, we have such system of equations $$\left\{\begin{aligned}2x=\lambda y\\2y=\lambda x\\xy=3\end{aligned}\right.,$$ and we get the point is $(\sqrt3,\sqrt3)$ or $(-\sqrt3,-\sqrt3)$.

Lagrange Multiplier

Now we conclude the method, given a function $f(x,y)$ and a constraint $g(x,y)=c$, then to maximize or minimize the function, we need to solve the system of equations $\left\{\begin{aligned}\frac{\partial f}{\partial x}&=\lambda\frac{\partial g}{\partial x}\\\frac{\partial f}{\partial y}&=\lambda\frac{\partial g}{\partial y}\\g(x,y)&=c\end{aligned}\right..$

Geometry

Why it is correct? If there is no constraint $g(x,y)=c$, we just solve $f_x=f_y=0$, and it means when we move on a horizontal surface near the point, the function doesn't change.
Now we have a constraint, similarly, we just find a point where we move along the constraint surface that the function doesn't change too. So $\nabla_{\hat\mathbf{u}}f=0$, where $\hat\mathbf{u}$ is any direction on the constraint surface, in other words, $\nabla f\cdot\hat\mathbf{u}=0$. Since the gradient $\nabla g$ is also normal to the constraint surface, we have $\nabla f\parallel\nabla g$.

Application

e.g. Find a best solution to minimizing the surface area of a pyramid with a given triangular base $a_1,a_2,a_3$ and a given height $h$.
Solution We can plot the pyramid in $xy$ plane.
pyramid-base.png
And it looks like the following in 3D space.
pyramid.png
To determine the position of the vertex $D$ more easily, we project the vertex on the $xy$ plane.
pyramid-vertex.png
And take the distance from the projection to three sides $a_1,a_2,a_3$ as $d_1,d_2,d_3$, and we can express the surface area $S$ and the base area $A$: $$\begin{aligned}S=\frac12a_1\sqrt{d_1^2+h^2}+\frac12a_2\sqrt{d_2^2+h^2}+\frac12a_3\sqrt{d_3^2+h^2}\\A=\frac12a_1d_1+\frac12a_2d_2+\frac12a_3d_3\end{aligned}.$$
And apply the Lagrange Multiplier $$\left\{\begin{aligned}\frac{\partial S}{\partial d_1}=\lambda\frac{\partial A}{\partial d_1}\\\frac{\partial S}{\partial d_2}=\lambda\frac{\partial A}{\partial d_2}\\\frac{\partial S}{\partial d_3}=\lambda\frac{\partial A}{\partial d_3}\end{aligned}\right.,$$ and found that $d_1=d_2=d_3$, so the vertex is just above the incenter of the triangular base.

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