Recursive Lagrange multipliers

In summary, the student is working with Lagrange multipliers and can get to the answer about half the time. However, when the multiplier equation becomes recursive, they are stuck. They attempted to solve the equation but got stuck. They followed what the author said and got the answer.
  • #1
hiigaranace
10
0
Hey all, this is my first post, so I apologize in advance if data are missing/format is strange/etc.

I'm working with lagrange multipliers, and I can get to the answer about half the time. The problem is, I'm not really sure how to deal with things when the multiplier equation becomes recursive, like in this problem:

Homework Statement


"Find the maximum and minimum values of x2+y2 subject to the constraint x2-2x+y2-4y=0"

Homework Equations


[tex]\grad{}[/tex]f(x, y) = [tex]\lambda\grad{}[/tex]g(x, y)
...those are supposed to be gradient symbols. They're a little different from what I'm used to seeing, but that's apparently what LaTeX thinks it should be.

The Attempt at a Solution


I let f be the equation to maximize and minimize, and let g the constraint equation. this gave me:

2x[tex]\vec{i}[/tex]+2y[tex]\vec{j}[/tex] = [tex]\lambda[/tex](2x-2)[tex]\vec{i}[/tex]+[tex]\lambda[/tex](2y-4)[tex]\vec{j}[/tex]

Then, you split it to deal with one direction at a time, giving two equations:

2x = [tex]\lambda[/tex](2x-2) 2y=[tex]\lambda[/tex](2y-4)

Annnnnnnd here's where I get stuck. I need to get everything in terms of one variable so I can put that into the constraint equation and solve for the coordinates, but when I try that, I either get X or Y on both sides of the equation (like above), or I wind up with the lambda isolated, but an expression for X or Y that necessitates their presence in the equation. I suspect I'm making some algebraic misstep (so weird, I can do calc, but algebra always kills me), rather than a calculus one, but I'm not sure. What should I do next?

Thanks!
 
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  • #2
I don't know what you mean by "recursive" here. There does not appear to me to be anything "recursive" here.

You want to maximize [itex]f(x,y)= x^2+ y^2[/itex] subject to the constraint that [itex]g(x,y)= x^2- 2x+ y^2- 4y= 0[/itex]

Okay, [itex]\nabla f= 2x\vec{i}+ 2y\vec{j}[/itex] and [itex]\nabla g= (2x- 2)\vec{i}+ (2x- 4)\vec{j}[/itex]. At a max or min with this constraint, we must have [itex]2x\vec{i}+ 2y\vec{j}= \lambda((2x-2)\vec{i}+ (2y-4)\vec{j})[/itex].

That is, we must have [itex]2x= \lambda(2x- 2)[/itex] and [itex]2y= \lambda(2y- 4)[/itex].

That, so far, is exactly what you have. One good way to solve those is to eliminate [itex]\lambda[/itex] by dividing one equation by the other.
x/y= (x-1)/(y- 2) or xy- 2x= xy- y so that xy= -y or y(x+ 1)= 0

I get four distinct points.
 
  • #3
I follow what you said, and I had a feeling it was something simple like that. I am slightly confused, though, about one of your simplifications of the division. You said that

xy-2x = xy-y gives xy = -y.

Shouldn't that be y = 2x, instead? If not, I'm not quite seeing how you got there.
 
  • #4
Never mind, I pressed ahead and got the answer. Thanks! Problems like this have been driving me nuts for a couple of days now...
 
  • #5
hiigaranace said:
Never mind, I pressed ahead and got the answer. Thanks! Problems like this have been driving me nuts for a couple of days now...

I've gotten y = 2x too ... Is it correct?
 
  • #6
icystrike said:
I've gotten y = 2x too ... Is it correct?

You're doing it right, icy, don't worry.
 

Related to Recursive Lagrange multipliers

1. What is the purpose of using Recursive Lagrange multipliers in optimization problems?

Recursive Lagrange multipliers are used to solve constrained optimization problems, where the objective function is subject to one or more equality constraints. It helps to find the optimal solution by converting the constrained problem into an unconstrained one, by introducing additional variables called Lagrange multipliers.

2. How does the Recursive Lagrange multiplier method work?

The Recursive Lagrange multiplier method involves finding the derivatives of the objective function and the constraints with respect to the Lagrange multipliers. These derivatives are then used to set up a system of equations, which is then solved to find the optimal values of the variables and Lagrange multipliers.

3. What are the advantages of using Recursive Lagrange multipliers?

Recursive Lagrange multipliers provide a systematic way to solve constrained optimization problems. It also allows for the inclusion of multiple constraints and can handle both equality and inequality constraints. Additionally, it provides a way to determine whether a given solution is optimal or not.

4. Can Recursive Lagrange multipliers be used for non-linear optimization problems?

Yes, Recursive Lagrange multipliers can be used for both linear and non-linear optimization problems. However, in the case of non-linear problems, the solution may not always be the global optimum, but rather a local optimum.

5. Are there any limitations to using Recursive Lagrange multipliers?

While Recursive Lagrange multipliers are a useful tool in solving constrained optimization problems, they can become computationally expensive when dealing with a large number of variables and constraints. Additionally, the method may not always converge to an optimal solution, especially in non-linear problems.

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