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**free variation**problem where it wasn't necessarily so before the elimination?

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- Thread starter jackiefrost
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In summary: So it seems that the same thing is happening with the Lagrange multiplier method - the constraining function is "tangent" to the function under examination so that the respective gradients are a real number multiple of the lamda.

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"As is familiar from elementary analysis, in finding an extremum of a function of n variables subject to k constraints (k<n), **we can use the constraints to express k variables in terms of the other n - k variables**. In this way, the problem is reduced to that of finding an unconstrained extremum of a function of n - k variables; i.e. an extremum subject to no subsidiary conditions."

I need to work out some simple examples to get the feel for why this works but it seems to me that this is a very amazing little mathematical "trick". Lagrange was pretty smart, huh? - #3

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A physical example of this would be to take the Lagrangian of a 2d pendulum system, written in Cartesian (not polar!) coordinates and substitute length = x^2 + y^2 for x in the Lagrangian. We know that this system has 1 degree of freedom and, now, the Lagrangian is expressed using one coordinate, y. When we look at variations of L with respect to some parameter, we will also be looking at variations in the expression sqrt(length - y^2), wherever x had appeared. Thus we are asserting the truth of both the constraint equation and L when we extremize.

The method of Lagrange multipliers is a little different, procedurally and conceptually, yet with similar results. Returning to the 2D pendulum, this method suggests that we set the variational derivative of L - \lambda * (x^2 + y^2) to 0. The reason for this is often explained geometrically, where, at a stationary point, the gradient of the two terms should be parallel (hence the lambda and the 0).

A good way to get a sense for these equations is to break them, in a controlled way. So, for example, what happens when you don't include any constraint information into the 2D Lagrangian? You get a constant momentum in the x-direction and the point mass either hits the V=0 level with a thud (if we asserted there is a ground) or oscillates about it in this odd mathematical construction. It is as if we snipped the string connecting the point mass to the pendulum pivot point. The first point to make is that we were successful in extremizing the Lagrangian without the constraint equation. We, in return, were given the EOMs (because these are the extremums) of a system that had no constraints. The second point is that this result is not consistent with the pendulum system we had in mind when we began, as it should be, for there was no way our Lagrangian could acquire the knowledge that "there is a string."

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Yes - and what mystifies me is the actual "mechanics" (in the mathematical sense) of how the lambda method accomplishes a reduction in the number of resulting differential equations from n to n-m if when we have n variables for the functional:derek e said:I guess the gist of it is that when you eliminate variables using constraint equations, you are incorporating information about your system into the problem. When extremizing the result, the information is utilized, consequently, during differentiation. i.e. You no longer regard certain variables as having independent variations with respect to the varying parameter, rather, you acknowledge the constraint, substitute in for the variable in question and observe the differential changes in the substituted expression.

F(x_{1},...,x_{n}),

and and m constraints,G_{j}(x_{1},...,x_{n}), {j=1,2,...,m},m<n.

Actually, Hey! I see what you mean here! I'm going to play with this today. Thanks a lot.derek e said:A physical example of this would be to take the Lagrangian of a 2d pendulum system...

[I'm kind of playing in my head with the geometrical notion of how constraints (as functionals) accomplish dimensional reduction and therefore path limitation in configuration space and your example is a perfect playground]

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It's probably best to split our peas and carrots. The method of Lagrange multipliers does not reduce the number of variables or the number of equations. What it does is incorporate the constraint information by adding a term to the constrained equation, then it observes a parallel gradient condition for singular points. In the 2D pendulum problem, one usually extremizes both the x and y coordinates, separately. On the other hand, solving the constraint for x and substituting this into the Lagrangian gives an expression free of x that can be normally extremized. The resulting y-EOM then gives the x-EOM without any further extremization. This sounds messy and I haven't tried it. The method of Lagrange multipliers is accessible in this problem, however.

There is a simple example of this problem in the mechanics book, by Hand and Finch, that I had used in school and I just picked it up and read that the authors suggest using the constraint length = sqrt(x^2 + y^2), rather than the length squared, because this gives a more interesting interpretation of the Lagrange multiplier.

https://www.amazon.com/dp/3540219250/?tag=pfamazon01-20 has a good section on the geometry of mechanics, as well. I also remember some Cal Tech notes that were floating around their physics site that handled this particular topic nicely. They were supplements (more like a step-by-step walk-through, lol) of Hand and Finch. Unfortunately, I do not remember the professor's name (I went to Berkeley).

There is a simple example of this problem in the mechanics book, by Hand and Finch, that I had used in school and I just picked it up and read that the authors suggest using the constraint length = sqrt(x^2 + y^2), rather than the length squared, because this gives a more interesting interpretation of the Lagrange multiplier.

https://www.amazon.com/dp/3540219250/?tag=pfamazon01-20 has a good section on the geometry of mechanics, as well. I also remember some Cal Tech notes that were floating around their physics site that handled this particular topic nicely. They were supplements (more like a step-by-step walk-through, lol) of Hand and Finch. Unfortunately, I do not remember the professor's name (I went to Berkeley).

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Yes. I was wrong about the reduction. Not sure why I thought that. In fact, in going back and re-studying what I have on this, now it seems that when finding the stationary value of a functional with n degrees of freedom, F(uderek e said:It's probably best to split our peas and carrots. The method of Lagrange multipliers does not reduce the number of variables or the number of equations. What it does is incorporate the constraint information by adding a term to the constrained equation, then it observes a parallel gradient condition for singular points.

At least, that's what I think today...

That looks like a great book! I wish I could afford ithttps://www.amazon.com/dp/3540219250/?tag=pfamazon01-20 has a good section on the geometry of mechanics, as well.

Communist :rofl:(I went to Berkeley).

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Lagrange multipliers are a mathematical tool used in calculus to optimize a function subject to constraints. They are used to find the maximum or minimum value of a function while satisfying a set of constraints.

To determine the critical points of a function using Lagrange multipliers, you must first set up the Lagrangian function by multiplying the original function by the constraints. Then, take the partial derivatives of the Lagrangian function with respect to all the variables. The critical points will be where all the derivatives equal zero.

Yes, Lagrange multipliers can be used for functions with multiple variables. In this case, the Lagrangian function will have multiple partial derivatives, and the critical points will be where all the derivatives equal zero.

The Lagrange multipliers represent the rate of change of the function with respect to the constraints. They can also be interpreted as the sensitivity of the function to changes in the constraints. A larger Lagrange multiplier indicates a higher sensitivity to changes in that particular constraint.

One limitation of using Lagrange multipliers is that they can only be used for continuous functions. Additionally, the constraints must be independent of the variables in the original function. Assumptions must also be made about the constraints, such as their differentiability and the existence of a global maximum or minimum.

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