Lagrange Multipliers in Calculus of Variations

In summary, Lagrangian mechanics uses Lagrange Multipliers to find the extrema of the action functional when there are more constraints than degrees of freedom. This is derived from Hamilton's principle and Lagrange's approach of finding a stationary point of a function. The Lagrange multiplier is added to the Lagrangian in order to incorporate the constraint function, and it can be shown that this is equal to the constraint forces. The uniqueness of the multiplier depends on the number of constraints and degrees of freedom.
  • #1
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In Lagrangian mechanics, can anyone show how to find the extrema of he action functional if you have more constraints than degrees of freedom (for example if the constraints are nonholonomic) using Lagrange Multipliers?

I've looked everywhere for this (books, papers, websites etc.) but none of them use the Calc. of Variations approach, they simply say something like "well assume a multiplier exists that makes this term zero" and go from there. I've never seen this derived from Hamilton's principle, which is what I think this is all about. I'll bet Lagrange looked at the problem in terms of finding a stationary point of a function, then applied the same idea to functionals. I'd like to see how this works, so I'd appreciate if someone could show me.

Thanks
 
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  • #2
Here's what I mean:

http://en.wikibooks.org/wiki/Classi...#Lagrange_multipliers_and_constraining_forces

They define a new Lagrangian,
[tex]\tilde L = L - \lambda(t)G(\boldsymbol x)[/tex]

Where they subtract the lagrange multiplier times the constraint function from the usual Lagrangian. Why? Can anyone show why this is?

From what I know, we have an action function defined:
[tex]S=\int_{t1}^{t2}f(q,q',t)dt[/tex]
and this function must be stationary for the correct path of the particle(s). For the unconstrained case, I know that S is stationary if the function integrated is the Lagrangian. But if it is constrained, can we do this?
[tex]\nabla S = \lambda \nabla G[/tex] for a constraint function G(q,q',t)
so then can we do this:
[tex]\frac{\partial S}{\partial q}=\lambda \frac{\partial G}{\partial q} , \frac{\partial S}{\partial q'}=\lambda \frac{\partial G}{\partial q'},\frac{\partial S}{\partial t}=\lambda \frac{\partial G}{\partial t} ?[/tex]

I'm stuck here...
 
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  • #3
1) You can't have more (non-redundant) constraints than degrees of freedom.
2) If you had the same number of constraints as degrees of freedom, the constraints would determine the entire motion.
3) You are free to add G because G is defined to be 0. You are adding zero to your Lagrangian.
 
  • #4
Then can you show that the [itex]\lambda(t)G(\boldsymbol x)[/itex] is equal to the constraint forces?

And can you show that the multiplier is unique? And under what conditions it is unique?
 
  • #5
What you would have is the standard Lagrangian formula:
[tex]{d \over dt}{\partial\tilde{L} \over \partial \dot q} - {\partial\tilde{L} \over \partial q} = 0[/tex]
 

1. What are Lagrange multipliers and how are they used in calculus of variations?

Lagrange multipliers are mathematical tools used in the calculus of variations to find the most optimal values of a function subject to one or more constraints. They help in finding the extreme values of a function while satisfying the given constraints.

2. How do Lagrange multipliers differ from other optimization techniques?

Lagrange multipliers are specifically used in the calculus of variations to optimize a function subject to constraints. Unlike other optimization techniques, they take into account the constraints while finding the extreme values of a function.

3. Can Lagrange multipliers be applied to functions with multiple variables?

Yes, Lagrange multipliers can be applied to functions with multiple variables. In such cases, the method involves finding partial derivatives and solving a system of equations to find the optimal values.

4. What are the necessary conditions for using Lagrange multipliers in the calculus of variations?

In order to use Lagrange multipliers, the function must be differentiable and the constraints must be continuous and differentiable as well. Additionally, the constraints must be independent and the Hessian matrix of the function must be positive definite at the extreme point.

5. Can Lagrange multipliers be applied to non-linear functions?

Yes, Lagrange multipliers can be applied to both linear and non-linear functions. However, the method may become more complex for non-linear functions as it involves finding partial derivatives and solving a system of non-linear equations.

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