Lagrange Multipliers HELP PLEASE

In summary: The points you found are sufficient. In summary, by using the equations f = (x-1)^2 + (y-1)^2 and g = x^2 + y^2 = 45, and setting the gradients of both equations equal to each other, it was found that the critical points for the boundary of the circle g are x = ±3 and y = ±6. These points were then plugged into the expression for f to determine that the points (-3,-6) and (3,6) correspond to a maximum and minimum value of f, respectively. Therefore, the maximum value of f is 4000 and the minimum value of f is 2.
  • #1
thanksie037
18
0

Homework Statement


Find the maximum and minimum values of f = (x-1)^2 + (y-1)^2 on the boundary of the circle g = x^2 + y^2 = 45.


Homework Equations



f=(x-1)^2 + (y-1)^2
g=x^2+y^2=45
gradf(x,y)=lambda*gradg(x,y)

The Attempt at a Solution



gradf(x,y)=<2x-2,2y-4>
gradg(x,y)=<2x,2y>
(1) 2x-2=lambda*2x
(2) 2y-4=lambda*2y
(3)g=x^2+y^2=45

solving the system for critical points:
x = 1/(1-lambda) plug into g?
y = 2/(1-lambda) plug into g?
gives lambda = 1 - (1/x) and lambda = 1 - (2/y)
set lambda = lambda:
1 - (1/x) = 1 - (2/y)
(1/x) = (2/y)
y = 2*x
here's where I get lost, how do i plus this back in? I shouldn't have to know the sqrt(45) to solve this. HELP
 
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  • #2
Well, IF [itex]y=2x[/itex], then [tex]x^2+y^2=x^2+(2x)^2=5x^2=45 \Rightarrow x= \pm 3[/tex]

But(!) you've made a mistake earlier in your calculations: Is [itex]\vec{\nabla}f(x,y)[/itex] really [itex]\left< 2x-2, \, 2y-4 \right>[/itex]? :wink:
 
  • #3
whoops f(x,y) = (x-1)^2+(y-2)^2
sorry about the typo
 
  • #4
once I've solved to find that x=(plus or minus)3, where do I plug it back into find the critical points? do I plug it into (1) and solve for lamba? or do i plus it into (3) again and solve for y?
 
  • #5
well, you plug it into y=2x first, so that you can find the corresponding y values...then you plug your (x,y) pairs into your expression for f and find out which ones are maximums and which are minumums...for example, if I found that f(3,6)=2 and f(-3,-6)=4000 then I would know that the point (-3,-6) is a maximum and the point (3,6) was a minimum and that the maximum and minimum values of f were 4000 and 2 respectively.
 
  • #6
do i need to find any more critical points? or will that suffice.. thank you for your help, by the way. your explanations have been clear, concise and beyond enlightening.
 
  • #7
Since all you are looking for are maximums and minimums, there are no other critical points to consider.
 

1. What are Lagrange multipliers and how are they used?

Lagrange multipliers are a mathematical tool used to find the maximum or minimum value of a function subject to constraints. They involve setting up an optimization problem with the function and constraints and using a special formula to solve for the optimal solution.

2. How do Lagrange multipliers work?

Lagrange multipliers work by introducing a new variable, known as a multiplier, to the objective function. This variable is then used to create a system of equations, known as the Lagrange equations, which can be solved to find the optimal solution that satisfies both the objective function and the constraints.

3. What types of problems can be solved using Lagrange multipliers?

Lagrange multipliers can be used to solve optimization problems in which a function needs to be maximized or minimized subject to certain constraints. This can include problems in economics, physics, engineering, and other fields.

4. What are the advantages of using Lagrange multipliers?

One advantage of using Lagrange multipliers is that they provide a systematic and efficient approach to solving optimization problems with constraints. They also allow for the inclusion of multiple constraints in a problem, making them versatile for a variety of applications.

5. Are there any limitations to using Lagrange multipliers?

One limitation of using Lagrange multipliers is that they may not always provide a unique solution to an optimization problem. In some cases, there may be multiple solutions or no solution at all. Additionally, the computation involved in solving for the optimal solution can become more complex with a larger number of constraints.

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