Minimization using Lagrange multipliers

In summary, the conversation discusses solving for expressions involving vector identities and integrals of the type $$E = \int d^3x L(\phi(x), \partial_i \phi(x))$$ which should be minimized. The method suggested is to rewrite the function ##L## in tensor notations and use Euler-Lagrange equations for fields. The equations (12) and (13) can then be derived from this approach.
  • #1
TheCanadian
367
13
Given the following expressions:

Screen Shot 2018-04-21 at 8.13.00 PM.png


and that ## \bf{B}_s = \nabla \times \bf{A}_s ##

how does one solve for the following expressions given in (12) and (13)?

Screen Shot 2018-04-21 at 8.13.11 PM.png


I've attempted doing so and derive the following expressions (where the hat indicates a unit vector):

## bV = \bf{ \hat{V}} \cdot {\bf{B}_s} + \bf{A}_s \cdot (\nabla \times \bf{\hat{V}}) ##

## {\bf{B}}_s \cdot (\nabla \times {\bf{\hat{A}}_s)} = B_s = ( \frac{1}{a} + \frac {1}{b}){B}_s + \frac{1}{2b}[\bf{V} \cdot (\nabla \times\bf{ \hat {A}}_s) + \bf{\hat {A}}_s \cdot (\nabla \times {\bf{V}})] ##

Although similar in some terms, this is clearly not equivalent to what's stated above after considering further vector identities and there is no curl(B) term present anywhere. It seems so simple yet my calculation is quite a bit off. If anyone could guide me through this with steps they've taken (and possibly ensure the above equations are actually correct), that would be kindly appreciated.
 

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  • #2
You have an integral of the type
$$E = \int d^3x L(\phi(x), \partial_i \phi(x))$$
which should be minimized. First, I suggest you to re-write the function ##L## inside the integral in tensor notations, for example ##\mathbf B = ∇ \times \mathbf A## is written as ##B_a = \epsilon_{abc}\partial_{b} A_c## (with ##\partial_i## I mean ##\frac {\partial}{\partial x_i}##). Once you have done that, use Euler-Langrange equations for fields https://en.wikipedia.org/wiki/Lagrangian_(field_theory) and Eq. (12) and (13) should follow. I didn't do it, but it seems to me it should work.
 

Related to Minimization using Lagrange multipliers

What is minimization using Lagrange multipliers?

Minimization using Lagrange multipliers is a mathematical technique used to find the minimum value of a function subject to a set of constraints. It involves introducing a new variable, called a Lagrange multiplier, to convert the constrained optimization problem into an unconstrained one.

What are the key steps in the minimization using Lagrange multipliers?

The key steps in minimization using Lagrange multipliers are:
1. Formulating the objective function and the constraints.
2. Constructing the Lagrangian function by multiplying each constraint by its corresponding Lagrange multiplier.
3. Differentiating the Lagrangian function with respect to the original variables and setting the derivatives equal to zero.
4. Solving the resulting system of equations to find the optimal values of the original variables and the Lagrange multipliers.
5. Checking the second-order conditions to ensure that the solution is a minimum.

What are the advantages of using Lagrange multipliers for minimization?

The advantages of using Lagrange multipliers for minimization include:
1. It allows for constrained optimization problems to be solved using methods developed for unconstrained problems.
2. It provides a systematic and efficient approach to solve optimization problems with multiple constraints.
3. It can handle nonlinear constraints and non-differentiable objective functions.
4. It can be extended to handle problems with inequality constraints.

What are some limitations of minimization using Lagrange multipliers?

Some limitations of minimization using Lagrange multipliers include:
1. It can only be applied to optimization problems with differentiable objective functions and constraints.
2. It may lead to a large number of Lagrange multipliers, making the problem computationally expensive.
3. It may not always guarantee a global minimum, as it only considers the local behavior of the objective function and constraints.

What are some real-world applications of minimization using Lagrange multipliers?

Minimization using Lagrange multipliers has various real-world applications, including:
1. In economics, it is used to optimize production processes subject to resource constraints.
2. In physics, it is used to optimize energy consumption in systems subject to physical laws.
3. In engineering, it is used to optimize designs subject to material and manufacturing constraints.
4. In machine learning, it is used to optimize objective functions subject to model complexity constraints.

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