Finding the minimum of an integral with Lagrange multipliers

In summary, the conversation discusses using Lagrange multiplier to minimize an integral, and using E-L equations to solve the problem. The conversation then goes on to show that the variational problem is equal to a differential equation, and the two problems are equivalent if and only if a certain condition is met.
  • #1
dRic2
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Homework Statement
I have to minimize the integral
$$\int_{\tau_1}^{\tau_2} y \sqrt{{x'}^2+{y'}^2} d \tau$$
with the isoperimetrical condition
$$\int_{\tau_1}^{\tau_2} \sqrt{{x'}^2+{y'}^2} = c$$
where ##c## is a constant.
Relevant Equations
Euler-Lagrange equations.
Using Lagrange multiplier ##\lambda## (only one is needed) the integral to minimize becomes
$$\int_{\tau_1}^{\tau_2} (y + \lambda) \sqrt{{x'}^2+{y'}^2} d \tau = \int_{\tau_1}^{\tau_2} F(x, x', y, y', \lambda, \tau) d\tau $$
Using E-L equations:
$$\frac {\partial F}{\partial x} - \frac d {d \tau} \frac {\partial F}{\partial x'} = 0 - \frac d {d \tau} \left( (y+\lambda) \frac {x'}{\sqrt{{x'}^2+{y'}^2}} \right) = 0$$
$$\frac {\partial F}{\partial y} - \frac d {d \tau} \frac {\partial F}{\partial y'} = \sqrt{{x'}^2+{y'}^2} - \frac d {d \tau} \left( (y+\lambda) \frac {y'}{\sqrt{{x'}^2+{y'}^2}} \right) = 0$$
$$\frac {\partial F}{\partial \lambda} - \frac d {d \tau} \frac {\partial F}{\partial \lambda'} = \sqrt{{x'}^2+{y'}^2} - 0 = 0$$
If I sum the equation for ##y## and ##\lambda## I get:
$$\frac d {d \tau} \left( (y+\lambda) \frac {y'}{\sqrt{{x'}^2+{y'}^2}} \right) = 0$$
Integrating the first equation I get:
$$(y+\lambda) \frac {x'}{\sqrt{{x'}^2+{y'}^2}} = \text{const.} \rightarrow y+\lambda = \frac {\sqrt{{x'}^2+{y'}^2}} {x'}$$
Putting together these last two equations I get:
$$\frac d {d \tau} \left( \frac {y'}{x'} \right) = \frac d {d \tau} \left( \frac {dy}{dx} \right) = 0$$
which is wrong!

I think I'm missing something fundamental because it is a pretty straightforward exercise.
 
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  • #2
For isoperimetric constraints, ##\lambda## is just a number, not a function to be varied with respect to.

I suggest you use the x-coordinate rather than path length as your curve parameter and apply the Beltrami identity.
 
Last edited:
  • #3
Orodruin said:
For isoperimetric constraints, λλ\lambda is just a number, not a function to be varied with emrespect to.
Dumb me... o:)o:) Thank you! ( btw I noticed that in my original post I wrote ##x## instead of ##\lambda##... nice deduction!)

Orodruin said:
I suggest you use the x-coordinate rather than path length as your curve parameter and apply the Beltrami identity.
I just need to show that this variational problem is equal to the differential equation:
$$c' \frac d {d \tau} \left( \frac {dy}{dx} \right) = 1$$
where ##c'## is an other constant.
Putting the first equation into the second and integrating:
$$\text{const} \frac {y'}{x'} = \int_{\tau_1}^{\tau_2} \sqrt{{x'}^2+{y'}^2} d \tau = c$$

So, I'd say the two problems are equivalent if and only if ##\frac {\tau_2 - \tau_1} {c'} = \frac {c} {\text{const}}##. What do you think ?
 

1. What is the purpose of using Lagrange multipliers in finding the minimum of an integral?

The purpose of using Lagrange multipliers is to find the minimum value of a function subject to a set of constraints. In the context of finding the minimum of an integral, Lagrange multipliers allow us to consider constraints on the variables involved in the integral and find the minimum value that satisfies those constraints.

2. How do Lagrange multipliers work in finding the minimum of an integral?

Lagrange multipliers work by introducing a new variable, known as the Lagrange multiplier, into the objective function. This variable is then used to incorporate the constraints into the function, allowing us to find the minimum value that satisfies those constraints.

3. What are the steps involved in using Lagrange multipliers to find the minimum of an integral?

The steps involved in using Lagrange multipliers to find the minimum of an integral are as follows:

1. Write the objective function in terms of the variables involved in the integral.

2. Write the constraints in the form of equations.

3. Introduce a Lagrange multiplier for each constraint and add them to the objective function.

4. Take the partial derivatives of the new function with respect to each variable and set them equal to 0.

5. Solve the resulting system of equations to find the minimum value of the integral.

4. What are the limitations of using Lagrange multipliers to find the minimum of an integral?

One limitation of using Lagrange multipliers is that it can only be used to find the minimum value of a function subject to constraints. It cannot be used to find the maximum value or to optimize a function without any constraints.

Another limitation is that it can be computationally intensive, especially for functions with multiple variables and constraints.

5. Can Lagrange multipliers be used to find the minimum of an integral with non-linear constraints?

Yes, Lagrange multipliers can be used to find the minimum of an integral with non-linear constraints. However, the process may be more complex and may require additional techniques such as the Kuhn-Tucker conditions.

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