Calculus of Variations & Lagrange Multiplier in n-dimensions

In summary, the conversation discusses solving for the equations of motion using the variational principle and applying constraints. In two dimensions, the equations can be simplified, but for more than two dimensions, the same manipulations cannot be used.
  • #1
MisterX
764
71
extremize
$$S = \int \mathcal{L}(\mathbf{y}, \mathbf{y}', t) dt $$
subject to constraint
$$g(\mathbf{y}, t) = 0 $$
We move away from the solution by
$$y_i(t) = y_{i,0}(t) + \alpha n_i(t) $$
$$\delta S = \int \sum_i \left(\frac{\partial\mathcal{L} }{\partial y_i} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_i'} \right)n_i(t) dt $$

In two dimensions we then have $$ \left(\frac{\partial\mathcal{L} }{\partial y_1} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_1'} \right)n_1(t) = \left(\frac{\partial\mathcal{L} }{\partial y_2} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_2'} \right)n_2(t)$$

and since $$ \delta g = 0 \Rightarrow \sum \frac{\partial g}{\partial y_i} n_i = 0$$
we have in the 2D case $$n_2 = \left(\frac{\partial g}{\partial y_2}\right)^{-1}\left(\frac{\partial g}{\partial y_1}\right)n_1
$$
and from this we obtain
$$\left(\frac{\partial g}{\partial y_1}\right)^{-1} \left(\frac{\partial\mathcal{L} }{\partial y_1} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_1'} \right) = \left(\frac{\partial g}{\partial y_2}\right)^{-1} \left(\frac{\partial\mathcal{L} }{\partial y_2} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_2'} \right) =-\lambda(t)$$
which leads us to the equations $$\frac{\partial\mathcal{L} }{\partial y_i} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_i'} + \left(\frac{\partial g}{\partial y_i}\right) \lambda(t) = 0 $$

However I am not sure how to do the case for more than 2 dimensions. It seems we have
$$n_j = \left(\frac{\partial g}{\partial y_j}\right)^{-1} \sum_{i\neq j}\frac{\partial g}{\partial y_i}n_i $$

So it seems maybe we can't do the same manipulations as for the 2D case.
 
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  • #2
Some corrections (I think)
$$ \left(\frac{\partial\mathcal{L} }{\partial y_1} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_1'} \right)n_1(t) = -\left(\frac{\partial\mathcal{L} }{\partial y_2} - \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial y_2'} \right)n_2(t)$$

$$n_j = - \left(\frac{\partial g}{\partial y_j}\right)^{-1} \sum_{i\neq j}\frac{\partial g}{\partial y_i}n_i $$
 

Related to Calculus of Variations & Lagrange Multiplier in n-dimensions

What is Calculus of Variations?

Calculus of Variations is a branch of mathematics that deals with finding the optimal function or curve that minimizes or maximizes a given functional. It is used to solve problems where the goal is to find the function that minimizes or maximizes a certain quantity, such as the path taken by a moving object that minimizes the time or energy required to reach its destination.

What is the concept of Lagrange Multiplier in n-dimensions?

Lagrange Multiplier in n-dimensions is a technique used in Calculus of Variations to find the extreme values of a function subject to a set of constraints. It involves creating a new function by adding a multiple of the constraints to the original function and then finding the critical points of this new function. The Lagrange Multiplier is the value of the multiplier that satisfies all the constraints and gives the extreme value of the original function.

What are the applications of Calculus of Variations & Lagrange Multiplier in n-dimensions?

Calculus of Variations & Lagrange Multiplier in n-dimensions have various applications in physics, engineering, economics, and other fields of science. They are used to solve optimization problems, such as finding the shortest path between two points, minimizing the energy or time required for a physical system to reach a desired state, and maximizing profits in economics. They are also used in control theory, where they help in designing optimal control systems.

What are the key principles of Calculus of Variations & Lagrange Multiplier in n-dimensions?

The key principles of Calculus of Variations & Lagrange Multiplier in n-dimensions are the Euler-Lagrange equation and the method of Lagrange multipliers. The Euler-Lagrange equation is used to find the critical points of the functional in question, while the method of Lagrange multipliers is used to incorporate constraints into the problem and find the extreme values of the function.

What are the limitations of using Calculus of Variations & Lagrange Multiplier in n-dimensions?

One limitation of using Calculus of Variations & Lagrange Multiplier in n-dimensions is that it can only be applied to functions that are continuously differentiable. Additionally, it may be difficult to find the critical points of the functional, especially in higher dimensions. Another limitation is that it may not always give the global optimum, and multiple solutions may exist.

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