Lagrange Multipliers to find max/min values

In summary: Since a specific value for \epsilon is not necessary for the solution, I find it is often simplest to start by eliminating \epsilon by dividing one equation by another. Here, start by dividing ye^{xy}= 3x^2\epsilon by xe^{xy}= 3y^2\epsilon: y/x= x^2/y^2 which is the same as x^3= y^3. Putting that into x^3+ y^3= 16 gives 2x^3= 16.well i sort of did something like that and got and y=x and with the whole x^3 + y^3 = 16 that means y=
  • #1
arl146
343
1

Homework Statement


Use Lagange Multipliers to find the max and min values of the function subject to the given constraint(s). f(x,y)=exp(xy) ; constraint: x^3 + y^3 = 16


Homework Equations


[itex]\nabla[/itex]f = [itex]\nabla[/itex]g * [itex]\lambda[/itex]
fx = gx * [itex]\lambda[/itex]
fy = gy * [itex]\lambda[/itex]


The Attempt at a Solution


Set the fx and fy eqns equal to 0. but i can't solve for x, y, and lambda... i guess my algebra isn't that strong

i got fx = [itex]\lambda[/itex] * gx
y*exy = 3x2[itex]\lambda[/itex]

and for y:

x*exy = 3y2[itex]\lambda[/itex]

and g(x,y) = x3 + y3 = 16
 
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  • #2
no one can offer a little help? =/
 
  • #3
Hi arl146! :smile:

You need to solve this set of equations:
(1) y exy = 3x2λ
(2) x exy = 3y2λ
(3) x3 + y3 = 16

Can you find λ from equation (1)?
And also from equation (2)?
Then equate them to each other, effectively eliminating λ?
 
  • #4
Since a specific value for [itex]\lambda[/itex] is not necessary for the solution, I find it is often simplest to start by eliminating [itex]\lambda[/itex] by dividing one equation by another. Here, start by dividing [itex]ye^{xy}= 3x^2\lambda[/itex] by [itex]xe^{xy}= 3y^2\lambda[/itex]: [itex]y/x= x^2/y^2[/itex] which is the same as [itex]x^3= y^3[/itex]. Putting that into [itex]x^3+ y^3= 16[/itex] gives [itex]2x^3= 16[/itex].
 
  • #5
well i sort of did something like that and got and y=x and with the whole x^3 + y^3 = 16 that means y=x=2. so there's what, is it called a critical pt still, at (2,2) ? so i just plug that into f(x,y)= exp(xy) ??
so you'd have f(2,2)=exp(4) ... is that all i do ? it just seems wrong.idk why haha
 
  • #6
Yes that's all you do. :)

The only thing remaining is finding out whether it's a maximum or a minimum...
 
  • #7
yea i think that's why i came here because i got confused with that one value. how do you know?
 
  • #8
arl146 said:
yea i think that's why i came here because i got confused with that one value. how do you know?

Doesn't your class material cover that?

Anyway, I know of 3 methods:

1. Using the second derivative test (Hessian matrix).
I can't quickly find a easy example for it (yet).

2. Since you only have one extrema, you can pick any point that satisfies the constraint and calculate f(x,y) there. Compare it with the f(x,y) at the extremum and you know whether it's a maximum or a minimum.

3. Vary x with a small epsilon, and calculate how you need to vary y to match the constraint in first order approximation.
In your case (x + epsilon)^3 + (y - epsilon)^3 = 16.
Check what f(x + epsilon, y - epsilon) does relative to f(x,y).
 

Q: What are Lagrange multipliers used for in finding maximum and minimum values?

A: Lagrange multipliers are used in multivariable calculus to find the maximum and minimum values of a function, subject to one or more constraints.

Q: How do Lagrange multipliers work in solving optimization problems?

A: Lagrange multipliers work by finding points on the boundary of the constraint region where the gradient of the function is parallel to the gradient of the constraint. These points are potential maximum or minimum values of the function.

Q: Can Lagrange multipliers be used for both unconstrained and constrained optimization problems?

A: Yes, Lagrange multipliers can be used for both unconstrained and constrained optimization problems. In unconstrained problems, the constraint function is simply set to be equal to zero.

Q: What is the Lagrange multiplier equation?

A: The Lagrange multiplier equation is ∇f = λ∇g, where ∇f represents the gradient of the objective function and ∇g represents the gradient of the constraint function.

Q: Are there any limitations or drawbacks to using Lagrange multipliers in optimization problems?

A: One limitation of Lagrange multipliers is that they can only be used for differentiable functions. Additionally, they can be computationally intensive for problems with multiple constraints.

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