Lagrange Multipliers: A theoretical question and an example

In summary, the conversation includes a request for help with three problems involving Lagrange multipliers. The first problem involves finding the condition for level curves to be tangent at a specific point. The second problem requires using the Lagrange multiplier method to maximize a function with a constraint. The third problem involves finding the minimum or maximum value of a function with given constraints. The conversation also includes a question about formatting and a request for input or corrections on their work.
  • #1
diewolke
2
0
Hello physicsforums community.
I have recently learned about Lagrange multipliers and have been given three problems to solve. Could you guys please go over my work and see if I have the gist of it? One question, a theoretical one, I have no idea how to begin. Any advice regarding this would be welcomed.

Thanks

PS: Please excuse any formatting errors; this is my first post on this forum.

--------------

Question 1a

f:R^2-->R^2, (x,y)-->x^2-y^2, and let S be the circle of radius 1 around the origin.
In two dimensions, the condition that
[tex]\nabla[/tex]f(x,y,z)=[tex]\lambda[/tex][tex]\nabla[/tex]g(x,y,z) at [tex]x_{o}[/tex], that is, [tex]\nabla[/tex]f(x,y,z) and [tex]\nabla[/tex]g(x,y,z) are parallel at [tex]x_{o}[/tex][/tex] is the same as the level curves being tangent at x[tex]_{.}[/tex]. Give the reason why you may conclude that the level curves are tangent at [tex]x_{o}[/tex].


1zqx0zt.jpg


2. f:R^2-->R^2, (x,y)-->x^2-y^2 and
g:R^2-->R^1, (x,y)--> x^2+y^2 (obtained from the statement about S)


3. I simply do not have a clue how to go about this. I know that the gradient of a function is given by the coordinates which are the function's partial derivatives. I also know that the gradient points to the direction of highest increase for a function at a particular point. Regarding level sets, I know that level sets are given by all (x,..x[tex]_{n}[/tex]) such that f(x,..,x[tex]_{n}[/tex])=c, where c is a constant. I am not sure how to utilize this information to produce an answer. Any advice?


---------------

Question 1b
1. Using the Lagrange multiplier method, maximize the function f(x,y,z)=x+z subject to the constraint x^2+y^2+z^2=51

2. [tex]\nabla[/tex]f(x,y,z)=[tex]\lambda[/tex][tex]\nabla[/tex]g(x,y,z)

3.
Having computed the gradients for g and f, listed the partials separately, and using the above equation, I obtained the following system of equations:
1=2x[tex]\lambda[/tex]
0=2y[tex]\lambda[/tex]
1=2z[tex]\lambda[/tex]

Solving for x, y, and z, I obtained
x=1/(2lambda)
y=0 and
z=x=1/(2lambda)

and here is the constraint once more: x^2+y^2+z^2=51
substituting the values of x, y, and z into the above expression,
I get [tex]\lambda[/tex]=+ or - [tex]\frac{1}{\sqrt{102}}[/tex]

using this value to solve for x, y, and z, I get that
x=+ or -[tex]\frac{\sqrt{102}}{2}[/tex]
y=0 and
z=+ or -[tex]\frac{\sqrt{102}}{2}[/tex]

plugging in these values into f(x,y,z)=x+z,
I find that the max must be [tex]\sqrt{102}[/tex]. Is this correct?

----------

I will post the third question shortly.

Thank you again
 
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  • #2
Question 2
1. Consider the function f(x,y)=5/x+1/y subject to the constraint
x+y=1, x>0, and y>0. Does this function with a constraint have a minimum or maximum? If so, what are these values?


2. [tex]\nabla[/tex]f(x,y)=[tex]\lambda[/tex][tex]\nabla[/tex]g(x,y)3. Finding the gradients for f and g and using the above formula, I obtained the following system of equations:
-5/x^2=[tex]\lambda[/tex]
-1/y^2=[tex]\lambda[/tex]

Solving for x and y,
x=[tex]\sqrt{-5/lambda[/tex] and
y=[tex]\sqrt{-1/lambda[/tex]

I discarded the negative valued solutions for x and y because of the given constraints that x and y are both greater than 0.

the constraint restated:
x+y=1

substituting x and y into this expression to solve for [tex]\lambda[/tex],
I get [tex]\lambda[/tex]=-2(3+sqrt{5})

solving for the numerical values of x and y using this [tex]\lambda[/tex],
I get that
x=sqrt(-5/(2(3+sqrt5))) and
y=sqrt(-1/(2(3+sqrt5)))

Plugging these values into f(x,y)=5/x+1/y I get a value of about 10.47.
I assume this to be a max since the other solutions for x and y (the negative values of what I found for x and y) had to be discarded due to the constraints that x and y > 0. These discarded x and y values would have produced a value less than 10.47, so I conclude that 10.47 is the max. Any input? Corrections? I expect more of the latter, haha.
 
Last edited:

Related to Lagrange Multipliers: A theoretical question and an example

1. What are Lagrange Multipliers?

Lagrange Multipliers are a mathematical tool used in multi-variable calculus to optimize a function subject to one or more constraints. They allow for the determination of the maximum or minimum value of a function while satisfying the given constraints.

2. Why are Lagrange Multipliers important?

Lagrange Multipliers are important because they provide a systematic approach to finding extrema (maximum or minimum) values of a function with constraints. They are widely used in physics, economics, engineering, and other fields to solve optimization problems.

3. How do Lagrange Multipliers work?

Lagrange Multipliers work by introducing a new variable, called a multiplier, into the original function and its constraints. This variable is then used to create a system of equations, known as the "Lagrangian equations," which can be solved to find the optimal solution.

4. Can you provide an example of using Lagrange Multipliers?

Yes, for example, suppose we want to find the maximum volume of a rectangular box with a fixed surface area of 100 square units. We can set up the function V = lwh (volume) and the constraint equation 2lw + 2lh + 2wh = 100 (surface area). By using Lagrange Multipliers, we can find the optimal values for l, w, and h that will give us the maximum volume of the box.

5. What are some common applications of Lagrange Multipliers?

Lagrange Multipliers have various applications, including in optimization problems in economics, physics, and engineering. They are also used in the field of machine learning to find the best model parameters that minimize the error in predictions. Additionally, they are used in statistics for finding the maximum likelihood estimates of model parameters.

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