Extreme values (Lagrange multipliers)

In summary, using Lagrange multipliers, we solve for the maximum and minimum values of the scalar field f(x,y) = xy subject to the constraint 4x^2 + 9y^2 = 36. The extreme points are (0,0), (\sqrt{\frac{18}{5}}, - \frac{2}{3}\sqrt{\frac{18}{5}}), and (- \sqrt{\frac{18}{5}}, \frac{2}{3}\sqrt{\frac{18}{5}}), with corresponding values of 0, -\frac{12}{5}, and \frac{12}{5}, respectively.
  • #1
aicort
6
0
determine, if any, the maximum and minimum values of the scalar field f (x, y) = xy subject to the constraint [tex]4x^2{}[/tex]+[tex]9y^2{}[/tex]=36


The attempt at a solution

using Lagrange multipliers, we solve the equations [tex]\nabla[/tex]f=[tex]\lambda[/tex][tex]\nabla[/tex]g ,which can be written as

[tex]f_{x}[/tex]=[tex]\lambda[/tex][tex]g_{x}[/tex]

[tex]f_{y}[/tex]=[tex]\lambda[/tex][tex]g_{y}[/tex]

g(x,y)=36

or as

y=[tex]\lambda[/tex]8x

x=[tex]\lambda[/tex]18y

[tex]4x^2{}[/tex]+[tex]9y^2{}[/tex]=36

it's pretty much all done but can somebody solve this? cause i have some doubts about which are the extreme points
 
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  • #2
I would say your [tex]g(x,y) = 36[/tex] is a little suspicious. If this were the case then the two statements above would be false since [tex]g_x[/tex] and [tex]g_y[/tex] are 0.
 
  • #3
no, in fact g(x,y)=[tex]4x^2{}[/tex]+[tex]9y^2{}[/tex]=36 but well, I wrote it that way because is the way my book does
 
  • #4
We already know that (0,0) is a turning point which has a value f (0,0) = 0.

Leaving that aside for now, solving the set of equations that you listed:
[tex]y = 8\lambda x[/tex]
[tex]x = 18\lambda y[/tex]
[tex]4x^2 + 9y^2 = 36[/tex]
would yield one or more points.

Then find the value of f(x,y) at each of these points and compare.
 
  • #6
Firstly, careless mistake: y is [tex]+/- \frac{2}{3}\sqrt{\frac{18}{5}}[/tex] as you've written on the third line, but in carrying it over into f(x,y), you mixed up the denominator and numerator.

Secondly, [tex]\lambda = +/- \frac{1}{12}[/tex], so there exist two other solutions:
[tex](x,y) = (\sqrt{\frac{18}{5}}, - \frac{2}{3}\sqrt{\frac{18}{5}})[/tex]
[tex](x,y) = (- \sqrt{\frac{18}{5}}, \frac{2}{3}\sqrt{\frac{18}{5}})[/tex]
 

What are extreme values in the context of Lagrange multipliers?

In the context of Lagrange multipliers, extreme values refer to the maximum or minimum values of a function subject to a set of constraints. These values are found using the method of Lagrange multipliers, which involves finding critical points of the function along with the corresponding Lagrange multipliers.

Why is the method of Lagrange multipliers used to find extreme values?

The method of Lagrange multipliers is used to find extreme values because it allows for the incorporation of constraints in the optimization process. This is important in cases where a function needs to be optimized subject to certain limitations or restrictions.

What is the relationship between Lagrange multipliers and gradients?

Lagrange multipliers and gradients have a close relationship in the context of finding extreme values. The gradient of a function represents its direction of steepest ascent, while the Lagrange multiplier represents the rate of change of the constraint. The two must be equal at the optimal solution, which can be found using the method of Lagrange multipliers.

What is the difference between a local and global extreme value?

A local extreme value is a maximum or minimum value of a function within a specific region or interval. It is only valid within that region and may not be the overall maximum or minimum of the entire function. A global extreme value, on the other hand, is the maximum or minimum value of the entire function, taking into account all possible values of the independent variable.

How can Lagrange multipliers be applied in real-world problems?

Lagrange multipliers can be applied in a variety of real-world problems, such as optimization in economics, engineering, and physics. For example, they can be used to find the minimum cost of production for a given output level, the maximum strength of a structure subject to certain constraints, or the minimum time it takes to complete a task under certain limitations.

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