Partial derivative used in Calc of Variation

In summary, Taylor is being lazy and using ##y## and ##y'## to stand for two different things. He writes:$$\begin{align}S(\alpha)&=\int_{x_1}^{x_2} f(Y, Y', x),dx \\&=\int_{x_1}^{x_2} f(y+\alpha\eta, y'+\alpha\eta', x),dx\end{align}$$But then he next writes:$$\frac {\partial} {\partial\alpha} f(y+\alpha\eta, y'+\alpha\eta', x) =\eta
  • #1
ibkev
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I'm working through the discussion of calculus of variations in Taylor's Classical Mechanics today. There's a step where partial differentiation is involved that I don't understand.

Given:
$$S(\alpha)=\int_{x_1}^{x_2} f(y+\alpha\eta, y'+\alpha\eta', x)\,dx$$

The goal is to determine ##y(x)## when the derivative of ##S(\alpha)=0## and ensure that ##\alpha=0## at this point.

So this means:
$$\frac {dS(\alpha)} {d\alpha}=\int_{x_1}^{x_2} \frac {\partial f} {\partial \alpha}\,dx=0$$

and now here is the step that I don't understand. It's apparently an application of the chain rule but I'm just not seeing it...
$$\frac {dS(\alpha)} {d\alpha}=\int_{x_1}^{x_2} \left( \eta\frac {\partial f} {\partial y}+\eta'\frac {\partial f} {\partial y'} \right)\,dx=0$$

Suggestions are appreciated! :)
 
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  • #2
ibkev said:
I'm working through the discussion of calculus of variations in Taylor's Classical Mechanics today. There's a step where partial differentiation is involved that I don't understand.

Given:
$$S(\alpha)=\int_{x_1}^{x_2} f(y+\alpha\eta, y'+\alpha\eta', x)\,dx$$

The goal is to determine ##y(x)## when both the derivative of ##S(\alpha)=0## and ensure that ##\alpha=0## at this point.

So this means:
$$\frac {dS(\alpha)} {d\alpha}=\int_{x_1}^{x_2} \frac {\partial f} {\partial \alpha}\,dx=0$$

and now here is the step that I don't understand. It's apparently an application of the chain rule but I'm just not seeing it...
$$\frac {dS(\alpha)} {d\alpha}=\int_{x_1}^{x_2} \left( \eta\frac {\partial f} {\partial y}+\eta'\frac {\partial f} {\partial y'} \right)\,dx=0$$

Suggestions are appreciated! :)

First, Taylor is being a bit lazy (although in a conventional way) and using ##y## and ##y'## to stand for two different things. In ##\frac {\partial f} {\partial y}##, he's using ##y## as the first argument of the the function ##f## and, likewise, ##y'## as its second argument.

But, the arguments are not simply ##y## and ##y'## so technically some additional notation is required.

There's a previous post here for the same question:

https://www.physicsforums.com/threads/euler-lagrange-derivation-taylor-series.852364/#post-5350620
 
  • #3
The integrand f is a function of two variables ##y## and ##y'##. The notation used is a little confusing because you end up re-using the same variables ##y## and ##y'## to describe the unperturbed trajectory and its unperturbed velocity.

To make it a little easier to see what's going on, you can change the notation a bit. Let ##u_{1}## and ##u_{2}## be coordinates for the position and velocity of a trajectory (any trajectory), and reserve the functions ##y(x)## and ##y'(x)## for a trajectory which makes the action integral S stationary. That makes f be a function of ##u_{1}## and ##u_{2}## in general, so it's written ##f(u_{1},u_{2})## in the new notation. That's how the chain rule comes into play. You want to find the trajectory ##(u_{1},u_{2}) = (y,y')##. You get the expression for ##S(\alpha)## by substituting ##(u_{1},u_{2}) = (y + \alpha \eta, y' + \alpha \eta')##. The final expression really says: $$\frac{dS(\alpha)}{d\alpha} = \int_{x_1}^{x_2} (\eta \frac{\partial f}{\partial u_{1}} + \eta' \frac{\partial f}{\partial u_{2}})dx$$
 
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  • #4
Ah ok - I'm starting to make sense of this now. Thanks!

The thing I find strange about how Taylor does this is that he actually sets up the problem at first similar to the ##(u_1, u_2)## in Twigg's post but calls them ##(Y, Y')## and then deliberately drops them.

He writes:
$$\begin{align}
S(\alpha)&=\int_{x_1}^{x_2} f(Y, Y', x),dx \\
&=\int_{x_1}^{x_2} f(y+\alpha\eta, y'+\alpha\eta', x),dx
\end{align}$$
But then he next writes:
$$\frac {\partial} {\partial\alpha} f(y+\alpha\eta, y'+\alpha\eta', x) =\eta\frac {\partial f} {\partial y} + \eta'\frac {\partial f} {\partial y'}$$
Do I understand right that he actually means for that previous equation to say:
$$\frac {\partial} {\partial\alpha} f(Y, Y' x)=\eta\frac {\partial f} {\partial Y} + \eta'\frac {\partial f} {\partial Y'}$$

>Taylor is being a bit lazy (although in a conventional way)
Honestly it seems like it should be an errata, no?
 
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  • #5
ibkev said:
Do I understand right that he actually means for that previous equation to say:
∂∂αf(Y,Y′x)=η∂f∂Y+η′∂f∂Y′​

Yeah, this is what he means, but it's also implied that ##Y = y + \alpha \eta## inside the argument of the function f. The variables ##Y## and ##Y'## represent coordinates in a geometric space whose axes are the position and velocity of the trajectory, so the substitution ##(Y,Y') = (y + \alpha \eta, y' + \alpha \eta')## is just a way of parametrizing that space the same way we could parametrize ordinary 3D space with ##\vec{r} = \vec{r_{0}} + \alpha \vec{u}## where ##\vec{u}## is an undetermined unit vector. In that picture, the expression you mentioned and I quoted above is just the directional derivative of f along the "direction" ##(\eta, \eta')## in the ##Y##-##Y'## plane.

I think Taylor intentionally dropped the Y,Y' notation because he probably didn't want to detract from the important points of this optimization procedure by making the theoretical distinction between y and Y, at the expense of confusing more cautious readers. While his notation is mathematically misleading, it still works and is a convenient shorthand.
 
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  • #6
That's a way of looking at it that I hadn't considered - thanks.

Incidentally, I checked how the Morin Classical Mechanics text does it and he carries the Y, Y' through to the very end (although he calls them ##(y_\alpha, y_\alpha')## and only then does he replace them with y, y' (albeit magically and without any explanation.)

After thinking about this, I came up with another way to look at this from the fact that there are TWO goals of our solution not one:
  1. solve ##\frac {dS(\alpha)} {d\alpha}=0## to find the local minimum
  2. require that ##\alpha=0## at that point as well
So the final step of the derivation recognizes that although we defined ##(Y,Y') = (y + \alpha \eta, y' + \alpha \eta')##, setting ##\alpha=0## at the end as our problem requires, results in ##(Y,Y')=(y,y')##

Does that seem like a reasonable way to look at it as well?
 
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  • #7
You are correct, and if you hadn't mentioned it I would've missed a second oversight in Taylor's math. Hamilton's principle does not say $$\frac{dS_{\alpha}}{d\alpha} = 0$$. That would would mean that all functions of the form ##y + \alpha \eta## where ##y## is a trajectory that makes the action integral stationary, are all stationary trajectories. What Hamilton's principle really says is this: $$\frac{dS_{\alpha}}{d\alpha}|_{\alpha = 0} = 0$$. In other words, it says that ##y## is the stationary trajectory, not just any trajectory of the form ##y + \alpha \eta##.
 
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1. What is a partial derivative used in the calculus of variation?

A partial derivative in the calculus of variation is a mathematical concept that measures the rate of change of a function with respect to one of its variables while holding all other variables constant. It is used to find the critical points of a function and determine the optimal value of a variable in a variation problem.

2. How is a partial derivative used to solve problems in the calculus of variation?

A partial derivative is used in the calculus of variation to find the stationary points of a function, which are points where the derivative is equal to zero. This helps in solving optimization problems and finding the optimal values of variables in a given system.

3. What is the difference between a partial derivative and a total derivative?

A partial derivative measures the rate of change of a function with respect to one variable while holding all other variables constant. On the other hand, a total derivative measures the overall rate of change of a function with respect to all of its variables. In other words, a partial derivative only considers changes in one variable, while a total derivative considers changes in all variables.

4. How is the concept of partial derivatives applied in real-world problems?

The concept of partial derivatives is applied in various fields such as physics, engineering, economics, and statistics to solve optimization problems. For example, in physics, it is used to find the path of least action in mechanics, while in economics, it is used to determine the optimal production levels in a firm.

5. Can a partial derivative be negative?

Yes, a partial derivative can be negative. A negative partial derivative indicates that the function is decreasing in the direction of that variable, while a positive partial derivative indicates an increase in the function. However, in the calculus of variation, we are interested in finding the critical points where the partial derivative is equal to zero, regardless of its sign.

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