Is My Calculus of Variations Approach Correct?

  • #1
erobz
Homework Helper
Gold Member
3,642
1,515
I would like to use the Calculus of Variations to show the minimum path connecting two points is a straight line, but I wish to do it from scratch without using the pre-packaged general result, because I'm having some trouble following it.

Points are ##(x_1,y_1),(x_2,y_2)##.

And we are to minimize this integral, whre ##y(x)## is the minimum path:

$$ \int_{x_1}^{x_2} ds = \int_{x_1}^{x_2} \sqrt{ 1 + y'(x)^2} dx $$

Then you make a new curve:

$$ Y(x) = y(x) + \beta \eta (x) $$

Differentiate ##Y(x)##:

$$ Y'(x) = y'(x) + \beta \eta '(x) $$

Sub into the integral and expand (dropping the function notation):

$$ I = \int_{x_1}^{x_2} \sqrt{ 1 + Y'(x)^2} dx = \int_{x_1}^{x_2} \sqrt{ 1 + y'^2 + 2 \beta y' \eta '+ \beta^2 \eta ' ^2 } dx $$

This you are supposed to take the derivative w.r.t. ##\beta##

$$ \frac{dI}{d \beta} = \int_{x_1}^{x_2} \frac{d}{d \beta}\left( \sqrt{ 1 + y'^2 + 2 \beta y' \eta '+ \beta^2 \eta ' ^2 } \right) dx $$

Am I doing this correctly to this point?
 
  • Like
Likes vanhees71
Physics news on Phys.org
  • #2
Do not forget that ##\eta(x_1)=\eta(x_2)=0##.
It seems that deducing the Lagrange equations in general form takes less writing than this masochism
 
Last edited:
  • Haha
Likes bhobba and vanhees71
  • #3
wrobel said:
Do not forget that ##\eta(x_1)=\eta(x_2)=0##.
It seems that deducing the Lagrange equations in general form takes less writing than this masochism
Agreed, and that's probably why I don't find example worked this way. But Im trying to get to the bottom of something.

My problem is that In Classical Mechanics by Taylor there is a point in the derivation of the Euler-Lagrange Equation that he says:

$$ \frac{\partial f ( y+\alpha \eta, y' + \alpha \eta ', x) }{\partial \alpha} = \eta \frac{\partial f }{ \partial y} + \eta ' \frac{\partial f }{ \partial y'} $$

I can't figure it out! ##y## doesn't depend on ##\alpha##?
 
  • #4
y does not depend on ##\alpha## yes
 
  • Like
Likes bhobba and vanhees71
  • #5
wrobel said:
y does not depend on ##\alpha## yes
Then that result is a mistake with notation?
 
  • #6
what "that"?
y is the fixed trajectory and ##y+\alpha \eta## its perturbation
 
  • Like
Likes vanhees71
  • #7
wrobel said:
what "that"?
post 3 is taken from Taylor.
 
  • #8
I do not see a problem with the formula from #3
 
  • Like
Likes bhobba and vanhees71
  • #9
wrobel said:
I do not see a problem with the formula from #3
we are to be differentiating ##f## with respect to ##\alpha##. ##y## does not depend on ##\alpha##.
 
  • #10
erobz said:
we are to be differentiating f with respect to α. y does not depend on α.
exactly!
 
  • Like
Likes bhobba and vanhees71
  • #11
wrobel said:
exactly!
Then #3 cannot be correct...
 
  • #12
erobz said:
we are to be differentiating ##f## with respect to ##\alpha##. ##y## does not depend on ##\alpha##.

In this case, [itex]\frac{\partial f}{\partial y}[/itex] means differentiation of [itex]f[/itex] with respect to its first argument, and [itex]\frac{\partial f}{\partial y'}[/itex] means differentiation of [itex]f[/itex] with respect to its second argument, in both cases with the other argument held constant.

You are differentiating not [itex]f[/itex], but the composite function [tex]g(y,y',\alpha) = f(y + \alpha \eta, y' + \alpha \eta')[/tex] with respect to [itex]\alpha[/itex]; by the chain rule this is then [tex]\frac{\partial g}{\partial \alpha} =
\frac{\partial f}{\partial y} \frac{\partial (y + \alpha \eta)}{\partial \alpha} + \frac{\partial f}{\partial y'} \frac{\partial (y' + \alpha \eta')}{\partial \alpha}.[/tex]
 
  • Like
Likes bhobba, vanhees71 and wrobel
  • #13
pasmith said:
In this case, [itex]\frac{\partial f}{\partial y}[/itex] means differentiation of [itex]f[/itex] with respect to its first argument, and [itex]\frac{\partial f}{\partial y'}[/itex] means differentiation of [itex]f[/itex] with respect to its second argument, in both cases with the other argument held constant.

You are differntiating the composite function [itex]f(y + \alpha \eta, y' + \alpha \eta')[/itex] with respect to [itex]\alpha[/itex]; by the chain rule this is then [tex]
\frac{\partial f}{\partial y} \frac{\partial (y + \alpha \eta)}{\partial \alpha} + \frac{\partial f}{\partial y'} \frac{\partial (y' + \alpha \eta')}{\partial \alpha}.[/tex]
The argument being ##y + \alpha \eta ##.

So it is a typo. He defines ## Y(x) = y(x) + \alpha \eta ## earlier, and what they really meant to say is:

$$ \frac{ \partial f ( Y,Y',x) }{\partial \alpha} = \eta \frac{\partial f }{ \partial Y} + \eta ' \frac{\partial f }{ \partial Y'}$$

?
 

1. What is the Calculus of Variations?

The Calculus of Variations is a branch of mathematics that deals with finding the optimal path or function for a given problem. It involves optimizing a functional, which is a function of a function, rather than a single variable.

2. How is the Calculus of Variations used in science?

The Calculus of Variations is used in many areas of science, including physics, engineering, and economics. It is particularly useful in problems involving optimization, such as finding the shortest path between two points or the shape of a curve that minimizes energy.

3. How do I know if my Calculus of Variations approach is correct?

To determine if your approach is correct, you should first make sure you have correctly formulated the problem and chosen appropriate boundary conditions. Then, you can use the Euler-Lagrange equation to find the necessary conditions for an optimal solution. If your solution satisfies these conditions, it is likely correct.

4. Can the Calculus of Variations be used for non-linear problems?

Yes, the Calculus of Variations can be used for both linear and non-linear problems. In fact, it is often used for non-linear problems where traditional calculus methods are not applicable.

5. Are there any limitations to the Calculus of Variations?

The Calculus of Variations is a powerful tool, but it does have some limitations. It may not always provide a unique solution, and in some cases, the optimal solution may not be achievable. Additionally, it can be computationally intensive for complex problems.

Similar threads

Replies
3
Views
1K
Replies
1
Views
940
Replies
5
Views
389
Replies
22
Views
458
Replies
6
Views
1K
Replies
4
Views
354
  • Special and General Relativity
Replies
4
Views
1K
  • Calculus and Beyond Homework Help
Replies
9
Views
549
Replies
3
Views
649
Replies
16
Views
2K
Back
Top