MHB GWR309's question at Yahoo Answers regarding Lagrange multipliers

Click For Summary
The discussion focuses on using Lagrange multipliers to optimize the function f(x,y) = xy under the constraint g(x,y) = x - 2y - 1 = 0. The critical point is found to be (x,y) = (1/2, -1/4), yielding a minimum value of f(1/2, -1/4) = -1/8. The method involves setting up a system of equations derived from the partial derivatives of the objective and constraint functions. To determine whether the critical point is a maximum, minimum, or neither, one can check the Hessian matrix or evaluate the function at other test points. This approach provides a systematic way to find extrema in constrained optimization problems.
MarkFL
Gold Member
MHB
Messages
13,284
Reaction score
12
Here is the question:

Please help with lagrange multipliers?

I don't understand how to do these at all. Here is one of the problems I have to do:

f(x,y) = xy x-2y=1Additional Details i thought I had more spaces when I typed... xy and x-2y=1 are 2 different equations.

Here is a link to the question:

Please help with lagrange multipliers? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
Mathematics news on Phys.org
Hello GWR309,

We are given the objective function:

$$f(x,y)=xy$$

subject to the constraint:

$$g(x,y)=x-2y-1=0$$

Using Lagrange multipliers, we get the system:

$$y=\lambda$$

$$x=-2\lambda$$

and this implies:

$$x=-2y$$

Substituting for $x$ into the constraint, we find:

$$-2y-2y-1=0$$

$$y=-\frac{1}{4}\,\therefore\,x=\frac{1}{2}$$

And so the critical point is:

$$(x,y)=\left(\frac{1}{2},-\frac{1}{4} \right)$$

and the optimize value for the objective function is:

$$f\left(\frac{1}{2},-\frac{1}{4} \right)=-\frac{1}{8}$$

Since other test points such as $$\left(0,-\frac{1}{2} \right)$$ and $$\left(1,0 \right)$$ give $$f(x,y)>-\frac{1}{8}$$ we may conclude that:

$$f_{\min}=f\left(\frac{1}{2},-\frac{1}{4} \right)=-\frac{1}{8}$$

As a check, we may use the constraint to write $f$ as a function of 1 variable, namely:

$$f(x)=\frac{x}{2}(x-1)$$

We can see now that $f(x)$ is a parabola opening upwards, and we know its vertex (which is the global minimum) will line on the axis of symmetry, which is midway between the roots, on the line:

$$x=\frac{1}{2}$$

and so the minimum is:

$$f_{\min}=f\left(\frac{1}{2} \right)=-\frac{1}{8}$$

To GWR309 and any other guests viewing this topic, I invite and encourage you to post other optimization problems in our http://www.mathhelpboards.com/f10/ forum.

Best Regards,

Mark.
 
MarkFL said:
Hello GWR309,

We are given the objective function:

$$f(x,y)=xy$$

subject to the constraint:

$$g(x,y)=x-2y-1=0$$

Using Lagrange multipliers, we get the system:

$$y=\lambda$$

$$x=-2\lambda$$

and this implies:

$$x=-2y$$

Substituting for $x$ into the constraint, we find:

$$-2y-2y-1=0$$

$$y=-\frac{1}{4}\,\therefore\,x=\frac{1}{2}$$

And so the critical point is:

$$(x,y)=\left(\frac{1}{2},-\frac{1}{4} \right)$$

and the optimize value for the objective function is:

$$f\left(\frac{1}{2},-\frac{1}{4} \right)=-\frac{1}{8}$$

Since other test points such as $$\left(0,-\frac{1}{2} \right)$$ and $$\left(1,0 \right)$$ give $$f(x,y)>-\frac{1}{8}$$ we may conclude that:

$$f_{\min}=f\left(\frac{1}{2},-\frac{1}{4} \right)=-\frac{1}{8}$$

As a check, we may use the constraint to write $f$ as a function of 1 variable, namely:

$$f(x)=\frac{x}{2}(x-1)$$

We can see now that $f(x)$ is a parabola opening upwards, and we know its vertex (which is the global minimum) will line on the axis of symmetry, which is midway between the roots, on the line:

$$x=\frac{1}{2}$$

and so the minimum is:

$$f_{\min}=f\left(\frac{1}{2} \right)=-\frac{1}{8}$$

To GWR309 and any other guests viewing this topic, I invite and encourage you to post other optimization problems in our http://www.mathhelpboards.com/f10/ forum.

Best Regards,

Mark.

Where did you get the system though? this:
y=λ

x=−2λ
 
GWR309 said:
Where did you get the system though? this:
y=λ

x=−2λ

Hello GWR309,

Glad you joined us here! (Cool)

The method of Lagrange multipliers states:

To find the extrema of $z=f(x,y)$ subject to the constraint $g(x,y)=0$, solve the system of equations:

$$f_x(x,y)=\lambda g_x(x,y)$$

$$f_y(x,y)=\lambda g_y(x,y)$$

$$g(x,y)=0$$

Among the solutions $(x,y,\lambda)$ of the system will be the points $$\left(x_i,y_i \right)$$, where $f$ has an extremum. When $f$ has a maximum (or minimum), it will be the largest (or smallest) number in the list of functional values $$f\left(x_i,y_i \right)$$.

So, given that:

$$f(x,y)=xy$$

$$g(x,y)=x-2y-1=0$$

we then find by computing the first partials, that:

$$f_x(x,y)=y,\,f_y(x,y)=x,\,g_x(x,y)=1,\,g_y(x,y)=-2$$

and so the system we are to solve is:

$$y=\lambda$$

$$x=-2\lambda$$

$$g(x,y)=x-2y-1=0$$

From the first two equations, we find:

$$\lambda=y=-\frac{x}{2}\,\therefore\,x=-2y$$

and the rest follows as in my second post above.
 
for general lagrange multipliers problems, how do I determine if it is a max, min or neither?
 
Last edited:
GWR309 said:
for general lagrange multipliers problems, how do I determine if it is a max, min or neither?

You could check the Hessian matrix. Or check that your function is convex (which would imply you have a minimum). Or even just substitute in the value and compare it to the endpoints of your function...
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

Similar threads

  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 1 ·
Replies
1
Views
10K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 8 ·
Replies
8
Views
2K
  • · Replies 11 ·
Replies
11
Views
3K
Replies
1
Views
2K
Replies
1
Views
2K
Replies
1
Views
2K
  • · Replies 16 ·
Replies
16
Views
3K