MHB Minimum of function under constraint

Click For Summary
The discussion focuses on minimizing the function g(x1, x2) = 2x1 + x2 under the constraint f(x1, x2) = x1 * x2 = 18. The critical points are found using both substitution and Lagrange multipliers, leading to the point (3, 6) as a local extremum. However, it is concluded that this point corresponds to a maximum rather than a minimum, as the function is not bounded below. The analysis also highlights the need to check boundaries and discontinuities in the function to determine the global behavior. Ultimately, the function does not have a global minimum due to its unbounded nature.
mathmari
Gold Member
MHB
Messages
4,984
Reaction score
7
Hey! :o

We want to minimize the function $g(x_1, x_2)=2x_1+ x_2$ under the constraint $f(x_1, x_2)=x_1\cdot x_2=18$.

\begin{equation*}x_1\cdot x_2=18 \Rightarrow x_1=\frac{18}{x_2}\end{equation*}

\begin{equation*}\tilde{g}(x_2)=g\left (\frac{18}{x_2}, x_2\right )=2\cdot \frac{18}{x_2}+ x_2= \frac{36}{x_2}+ x_2\end{equation*}

\begin{equation*}\tilde{g}'(x_2)=-\frac{36}{x_2^2}+1=\frac{-36+x_2^2}{x_2^2}\end{equation*}

\begin{equation*}\tilde{g}'(x_2)=0 \Rightarrow \frac{-36+x_2^2}{x_2^2}=0 \Rightarrow -36+x_2^2=0 \Rightarrow x_2^2=36 \Rightarrow x_2=\pm 6\end{equation*}

\begin{equation*}\tilde{g}''(x_2)=-\frac{36}{x_2^2}=\frac{72}{x_2^3}\end{equation*}

For $x=6$ we get $\tilde{g}''(6)=\frac{72}{6^3}=\frac{1}{3}>0$ and for $x=-6$ we get $\tilde{g}''(-6)=\frac{72}{-6^3}=-\frac{1}{3}<0$.

So, $\tilde{g}$ has a minimum at $x_2=6$.

The fuction $g$ has theerfore the minimum at $\left (\frac{18}{x_2}, x_2\right ) =(3, 6)$.

In Wolfram it says that this is the maximum.

What have I done wrong? (Wondering)
 
Last edited by a moderator:
Physics news on Phys.org
The function:

$$f(x)=\frac{36}{x}+x$$

has 2 local extrema, but we need to also look at the range, which is $(-\infty,-6]\,\cup\,[6,\infty)$. You correctly found the local minimum for $0<x$, bur the local maximum for $x<0$ is $-6$. :D
 
MarkFL said:
The function:

$$f(x)=\frac{36}{x}+x$$

has 2 local extrema, but we need to also look at the range, which is $(-\infty,-6]\,\cup\,[6,\infty)$. You correctly found the local minimum for $0<x$, bur the local maximum for $x<0$ is $-6$. :D

So, do we not get in this case the result using the second derivative test? (Wondering)
 
You also need to check the boundaries of the function, one of which is $x=0$, at which the function is discontinuous. Another way to find the range of the function $f$ I gave is by writing:

$$fx=36+x^2$$

$$x^2-fx+36=0$$

We then know (from the discriminant) that we require:

$$f^2-12^2\ge0$$

$$(f+12)(f-12)\ge0$$

Hence, we find $f$ is on:

$$(-\infty,-12]\,\cup\,\,[12,\infty)$$

In my haste earlier, I gave the incorrect range.

So, while $f$ has a local min. of $12$ at $x=6$, this is not the global minimum, as the left branch of the function lies wholly below that value, i.e. when $x<0$, then $-\infty<f\le-12$.
 
MarkFL said:
You also need to check the boundaries of the function, one of which is $x=0$, at which the function is discontinuous.

How do we find the boundaries? (Wondering)
MarkFL said:
Another way to find the range of the function $f$ I gave is by writing:

$$fx=36+x^2$$

$$x^2-fx+36=0$$

We then know (from the discriminant) that we require:

$$f^2-12^2\ge0$$

$$(f+12)(f-12)\ge0$$

Hence, we find $f$ is on:

$$(-\infty,-12]\,\cup\,\,[12,\infty)$$

In my haste earlier, I gave the incorrect range.

So, while $f$ has a local min. of $12$ at $x=6$, this is not the global minimum, as the left branch of the function lies wholly below that value, i.e. when $x<0$, then $-\infty<f\le-12$.
Since $-\infty <f\leq -12$ the function is not bounded from below, is it? (Wondering)
 
Yes, the function as a whole is not bounded from below or from above. Let's look at this using Lagrange Multipliers...

We have the objective function:

$$f(x,y)=2x+y$$

Subject to the constraint:

$$g(x,y)=xy-18=0$$

We then obtain the system:

$$2=\lambda y$$

$$1=\lambda x$$

This implies:

$$y=2x$$

Substituting this into the constraint yields:

$$x^2=9\implies x=\pm3$$

So, this does yield the two critical points you found by using the constraint to write the objective function in one variable:

$$(\pm3,\pm6)$$

However, these two points correspond only to local extrema, not global. If we turn now to the objective function in one variable:

$$f(x)=2x+\frac{18}{x}=2\cdot\frac{x^2+9}{x}$$

And differentiate while equating the result to zero to obtain critical values:

$$f'(x)=2\left(\frac{2x^2-(1)\left(x^2+9\right)}{x^2}\right)=0$$

$$\frac{x^2-9}{x^2}=0$$

We must observe that we obtain critical values not only when the numerator is zero, but also when the denominator is zero. And we observe that:

$$\lim_{x\to0^{-}}y=-\infty$$

$$\lim_{x\to0^{+}}y=\infty$$

So we therefore may conclude that the objective function has no global extrema. :D
 
Ah ok! How could we plot this function to see the local extremas? With desmos we cannot, can we? (Wondering)
 
mathmari said:
Ah ok! How could we plot this function to see the local extremas? With desmos we cannot, can we? (Wondering)

I would use W|A:

W|A - optimize 2x+y subject to xy=18
 
  • #10
mathmari said:
Hey! :o

We want to minimize the function $g(x_1, x_2)=2x_1+ x_2$ under the constraint $f(x_1, x_2)=x_1\cdot x_2=18$.
Another way, using "Lagrange multipliers": With $g(x, y)= 2x+ y$ with $f(x, y)= xy$ then $\nabla g= 2\vec{i}+ \vec{j}$ and $\nabla f= y\vec{i}+ x\vec{j}$. At an extremum those gradient vectors must be parallel: $2\vec{i}+ \vec{j}= \lambda(y\vec{i}+ x\vec{j})$. ($\lambda$ is the "Lagrange multiplier".) So we must have $2= \lambda y$ and $1= \lambda x$. Together with the constraint, $xy= 18$, those are three equations to solve for x, y, and $\lambda$. But since a value for $\lambda$ is not necessary for a solution, a good way to start is to eliminate $\lambda$ from those equations by dividing one by the other: $\frac{2}{1}= \frac{y}{x}$ or $y= 2x$. Then $xy= x(2x)= 2x^2= 18$, $x^2= 9$, $x= 3$ or $x= -3$. Only $x= 3$ gives a point in the first quadrant. Further, $y= 2(3)= 6$. The only extremum in the first quadrant is (3, 6). To see that this gives a maximum, not that g(3, 6)= 2(3)+ 6= 12 while g(1, 1)= 2(1)+ 3= 5, a smaller value. The point is that since g(1, 1)< g(3, 6), (3, 6) cannot give a minimum so must give a maximum.
 
  • #11
HallsofIvy said:
Another way, using "Lagrange multipliers": With $g(x, y)= 2x+ y$ with $f(x, y)= xy$ then $\nabla g= 2\vec{i}+ \vec{j}$ and $\nabla f= y\vec{i}+ x\vec{j}$. At an extremum those gradient vectors must be parallel: $2\vec{i}+ \vec{j}= \lambda(y\vec{i}+ x\vec{j})$. ($\lambda$ is the "Lagrange multiplier".) So we must have $2= \lambda y$ and $1= \lambda x$. Together with the constraint, $xy= 18$, those are three equations to solve for x, y, and $\lambda$. But since a value for $\lambda$ is not necessary for a solution, a good way to start is to eliminate $\lambda$ from those equations by dividing one by the other: $\frac{2}{1}= \frac{y}{x}$ or $y= 2x$. Then $xy= x(2x)= 2x^2= 18$, $x^2= 9$, $x= 3$ or $x= -3$. Only $x= 3$ gives a point in the first quadrant. Further, $y= 2(3)= 6$. The only extremum in the first quadrant is (3, 6). To see that this gives a maximum, not that g(3, 6)= 2(3)+ 6= 12 while g(1, 1)= 2(1)+ 3= 5, a smaller value. The point is that since g(1, 1)< g(3, 6), (3, 6) cannot give a minimum so must give a maximum.

I see! Thank you very much! (Sun)
 

Similar threads

Replies
6
Views
3K
Replies
2
Views
2K
Replies
4
Views
2K
Replies
4
Views
2K
Replies
3
Views
2K
Replies
3
Views
3K
Replies
0
Views
547