Problem understading lagrange multipliers

In summary, the quick and dirty way to find a local maximum for a function is to "follow" the function's projection onto a surface and look for the point where the projection is zero.
  • #1
Narcol2000
25
0
I'm trying to follow the idea behind Lagrange multipliers as given in the following wikipedia link.

http://en.wikipedia.org/wiki/Lagrange_multipliers

I follow the article right up until the point where it goes:

'To incorporate these conditions into one equation, we introduce an auxiliary function:'
*<insert formula seemingly plucked from random here>*

There doesn't seem to be any explanation or reason why this formula was chosen, and the 'justification' section just repeats what the constraints are, but still doesn't say where the formula comes from...

any ideas?
 
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  • #2
The true way is given in many books.
The quick and dirty way:

Thing to minimize: f(x,y)
Constraint equation: g(x,y)=A

We know we need to get the constraint equation into the thing to minimize, so we rearrange it to 0=A-g(x,y). We can always add zero to anything, so the thing to minimize becomes:

Thing to minimize taking constraint into account: f(x,y)+lamda(A-g(x,y))

where lamda is the Lagrange multiplier which you use to enforce the constraint after minimization.
 
Last edited:
  • #3
Thanks atyy, makes sense.
:)
 
  • #4
Here's another way of thinking about it. Suppose you wanted to find a local maximum for f(x,y,z). One way to do that is to pick some starting point, find [itexs]\nabla f[/itex] at that point and "follow" that vector a short distance. Because [itex]\nabla f[/itex] points in the direction of fastest increase, that should take you closer to the maximum point (a short distance) because you don't want to overshoot). You can keep doing that as long as [itex]\nabla f[/itex] is not the 0 vector. That shows that a maximum (or minimum) must occur where [itex]\nabla f= 0[/itex].

Now suppose you are constrained to stay on the surface g(x,y,z)= constant. Now you can't "follow" the vector- it might point off the surface. But you can look at the projection of [itex]\nabla f[/itex] in the surface and get closer to the maximum value by going that way. You can do that until the projection of [itex]\nabla f[/itex] in the surface is 0- which is the same as saying that [itex]\nabla f[/itex] is perpendicular to the surface. Of course, [itex]\nabla g[/itex] is also perpendicular to the surface g(x,y,z)= constant. That means that a maximum (or minimum) will occur where [itex]\nabla f[/itex] and [itex]\nabla g[/itex] are parallel. And that is the same as saying one is a multiple of the other: [itex]\nabla f= \lambda\nabla g[/itex].
 

What is the purpose of using Lagrange multipliers in problem understanding?

The main purpose of using Lagrange multipliers is to find the optimal solution to a constrained optimization problem. It allows us to incorporate the constraints into the objective function and find the values of the variables that satisfy both the objective function and the constraints.

How do Lagrange multipliers work?

Lagrange multipliers work by creating a new objective function to optimize, known as the Lagrangian, by adding a multiple of each constraint to the original objective function. By taking the partial derivatives of the Lagrangian with respect to the variables and the multiplier, we can find the optimal values that satisfy both the objective function and the constraints.

What are the benefits of using Lagrange multipliers?

Using Lagrange multipliers allows us to solve constrained optimization problems without having to manipulate the constraints. It also provides a systematic method for finding the optimal solution and can be applied to problems with multiple constraints.

When should Lagrange multipliers be used?

Lagrange multipliers should be used when solving constrained optimization problems, where the constraints cannot be easily incorporated into the objective function. It is also useful in cases where there are multiple constraints and finding the optimal solution using other methods may be difficult or time-consuming.

Are there any limitations to using Lagrange multipliers?

One limitation of using Lagrange multipliers is that it may not always provide the global optimal solution, especially in cases where there are multiple local optimal solutions. It also requires the constraints to be differentiable, which may not always be the case in real-world problems.

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