Lagrange Multiplier MethodMaking Sense of the Results

In summary, the conversation is about solving a Lagrange multiplier problem with 2 constraints and determining the points that satisfy both constraints. The solution gives four points, but the person is wondering if there should be eight points. Another person explains that back checking is necessary to ensure all points are valid for both constraints. They also mention using WolframAlpha to help with finding maximum and minimum values.
  • #1
Saladsamurai
3,020
7

Homework Statement



I am doing this lagrange multiplier problem with 2 constraints. I have completely solved it as shown in the image below. I have found that for lambda = 1 and mu = +/- 1/2 I have that x=+/- [sqrt(2)] y=+/- [1/sqrt(2)] and z=+/- [1/sqrt(2)].

So I am trying to figure out what points I actually have now. It seemed to me that since for x,y,z I have both a positive and negative value, I should have 2*2*2= 8 points to look at. But the solution only lists four. Am I messing this up somehow? Are there not 8 points given by the solution below? Thanks.

TTT1.jpg
 
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  • #2
That's pretty good. But how many of those 8 points satisfy xy=1?
 
  • #3
Dick said:
That's pretty good. But how many of those 8 points satisfy xy=1?

Ah ha. I see now. Thanks Dick! Is there a general approach to keeping track of which points are valid for all constraints? Or do you just solve the n equations for n unknowns and then back-check? I know there is probably no blanket rule.. but is that the approach more times than not?
 
  • #4
Saladsamurai said:
Ah ha. I see now. Thanks Dick! Is there a general approach to keeping track of which points are valid for all constraints? Or do you just solve the n equations for n unknowns and then back-check? I know there is probably no blanket rule.. but is that the approach more times than not?

You've got it. No, I don't think there's any more general way than back checking. Your solutions from solving subsets of the equations may give you extraneous solutions. Just back check.
 
  • #5
Thanks a bunch! :smile:
 
  • #7
Haha! Nice one cronxeh :smile:
 

1. What is the Lagrange Multiplier Method used for?

The Lagrange Multiplier Method is a mathematical technique used to optimize a function subject to constraints. It is commonly used in physics, economics, and engineering to solve optimization problems.

2. How does the Lagrange Multiplier Method work?

The method involves calculating the partial derivatives of the objective function and the constraints, and then setting them equal to each other. This creates a system of equations that can be solved to find the optimal values for the variables.

3. How do you interpret the results of the Lagrange Multiplier Method?

The results of the method provide the optimal values for the variables that maximize or minimize the objective function while satisfying the given constraints. The Lagrange multiplier is also an important factor in interpreting the results as it represents the rate of change of the objective function along the constraint curve.

4. What are the limitations of the Lagrange Multiplier Method?

One limitation of the method is that it can only be used for problems with continuous functions and constraints. It also requires the objective function and constraints to be differentiable. Additionally, the method may not provide the global optimum for non-convex functions.

5. Are there any real-world applications of the Lagrange Multiplier Method?

Yes, the method has a wide range of applications in various fields such as engineering, economics, and physics. It is commonly used in optimization problems such as finding the maximum profit for a company while satisfying production constraints or determining the optimal shape of a structure subject to material constraints.

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