Help with Euler Lagrange equations: neighboring curves of the extremum

In summary, the conversation discusses the reason for needing u to be small in the Taylor expansion, and the unsoken assumption that the taylor series is being truncated. The conversation also mentions the necessary condition for deriving the Euler-Lagrange equations and provides a proof without the use of u being small. However, the other person in the conversation suggests a simpler method using the fundamental theorem of variational calculus, which leads to the same conclusion.
  • #1
Reuben_Leib
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Euler Lagrange equations
I tried writing this out but I think there is a bug or something as its not always displaying the latex, so sorry for the image.
I have gone through various sources and it seems that the reason for u being small varies. Sometimes it is needed because of the taylor expansion, this time (below) is "needed" for equations(2.18) and (2.21). I provided all the information I think you may need. So in this case, why must u be small?

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  • #2
The unsoken assumption is that you are truncating the taylor series of [itex]x(t,u)[/itex] with respect to [itex]u[/itex] about [itex]u = 0[/itex]: [tex]
x(t, u) = x(t,0) + u\left.\frac{\partial x}{\partial u}\right|_{u = 0} + \frac12 u^2 \left.\frac{\partial^2 x}{\partial u^2}\right|_{u = 0} + \dots[/tex] where here [tex]\eta = \left.\frac{\partial x}{\partial u}\right|_{u=0}.[/tex] The neglect of the higher order terms is only justified if [itex]u[/itex] is small. But really what you are looking for to derive the Euler-Lagrange equations is a functional derivative at [itex]x[/itex] in the direction of [itex]\eta[/itex], [tex]
\delta I = \lim_{u \to 0} \frac{I[x + u\eta] - I[x]}{u} = \left(\left.\frac{d}{du}I[x + u\eta]\right|_{u = 0}\right)
[/tex] and in this formulation [itex]x(t,u) = x(t,0) + u \eta(t)[/itex] is exact, not approximate.
 
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  • #3
Thank you pasmith for your reply, From my understanding I believe that equations(2.18) and (2.21) should hold regardless of the size of ##u##? Maybe I am not understanding taylor series correctly, but I thought that for an example a first order taylor expansion: $$I(u) = I(0) + uI'(0) + \frac{I''(\epsilon)}{2}u^2$$ where ##\epsilon \in [0,u]##, is exact?

standardly let: $$I(u) = I(0) + uI'(0) + O(u^2)$$

The I can go on to prove the(the necessary condition) that ##\delta I = 0## :

Suppose $I(0)$ is the extremum is a minimum, then proof by contradiction: let $I'(0) > 0$.
Let ##C## be chosen so that ##C \geq \frac{I''(\epsilon)}{2}## for all ##u \in [-U,U]##, thus we get ##Cu^2 \geq \frac{I''(\epsilon)}{2}u^2 = O(u^2)##.
Now we can get ##|I(u)-I(0)-uI'(0)| = O(u^2) \leq Cu^2##
Now let ##u<0##
As ##I'(0)## is a constant(not dependent on ##u##),chose ##u## such that ##C|u|=\frac{I'(0)}{4} < \frac{I'(0)}{2}##
Letting ##C|u| \leq \frac{I'(0)}{2}##, thus ##-\frac{I'(0)}{2}\leq Cu \leq \frac{I'(0)}{2}##, thus ##-u\frac{I'(0)}{2}\geq Cu^2 \geq \frac{I'(0)}{2}u##, because ##u<0##.
Now as ##|I(u) -I(0) - uI'(0)|\leq Cu^2##, thus ##-Cu^2\leq I(u) -I(0) - uI'(0)\leq Cu^2##. Taking ##I(u)\leq I(0) + uI'(0)+Cu^2##. and ##Cu^2\leq-u\frac{I'(0)}{2}## we get:##I(u)\leq I(0) + uI'(0)+Cu^2\leq f(0) + u\frac{I'(0)}{2}< I(0)##
Thus ##I(0)## can't be minimum, hence ##I'(0) \leq 0##, similar to prove for maximum ##I'(0) \geq 0##, thus ##I'(0) = 0##
So this proves that ##I'(0) = 0##.

So from this I believe that I proved then necessary condition without the use of ##u## being small?
 
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  • #4
It's much easier to argue just with the finding of extrema of functions of a real variable. You simply define
$$F(u)=\int_{t_1}^{t_2} \mathrm{d} t [L(\vec{x} + u \vec{\eta},\dot{\vec{x}}+u \dot{\vec{\eta}}].$$
Then for this to get extremal at ##\eta=0## you must have
$$F'(u)|_{u=0}=0.$$
Evaluate the derivative
$$F'(u)=\int_{t_1}^{t_2} \mathrm{d}t \left [\vec{\eta} \cdot \frac{\partial L}{\partial \vec{x}} + \dot{\vec{\eta}} \cdot \frac{\partial L}{ \partial \dot{\vec{x}}} \right].$$
Next you use that in the Hamilton principle (in Lagrangian form) the endpoints of the trajectories under consideration are held fixed, i.e., you have ##\vec{\eta}(t_1)=\vec{\eta}(t_2)=0##. Then you can integrate the 2nd contribution by parts and get
$$F'(u)=\int_{t_1}^{t_2} \mathrm{d}t \vec{\eta} \cdot \left [\frac{\partial L}{\partial \vec{x}} - \frac{\mathrm{d}}{\mathrm{d} t} \frac{\partial L}{ \partial \dot{\vec{x}}} \right].$$
Now set ##u## to 0 and then, because ##F'(u=0)=0## must hold for all trajectories ##\vec{\eta}(t)##, the bracket in the integral must vanish (this is known as the "fundamental theorem of variational calculus"), which leads to the Euler-Lagrange equations,
$$\frac{\mathrm{d}}{\mathrm{d} t} \frac{\partial L}{ \partial \dot{\vec{x}}}=\frac{\partial L}{\partial \vec{x}}.$$
 
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  • #5
vanhees71 said:
It's much easier to argue just with the finding of extrema of functions of a real variable. You simply define
$$F(u)=\int_{t_1}^{t_2} \mathrm{d} t [L(\vec{x} + u \vec{\eta},\dot{\vec{x}}+u \dot{\vec{\eta}}].$$
Then for this to get extremal at ##\eta=0## you must have
$$F'(u)|_{u=0}=0.$$
Evaluate the derivative
$$F'(u)=\int_{t_1}^{t_2} \mathrm{d}t \left [\vec{\eta} \cdot \frac{\partial L}{\partial \vec{x}} + \dot{\vec{\eta}} \cdot \frac{\partial L}{ \partial \dot{\vec{x}}} \right].$$
Next you use that in the Hamilton principle (in Lagrangian form) the endpoints of the trajectories under consideration are held fixed, i.e., you have ##\vec{\eta}(t_1)=\vec{\eta}(t_2)=0##. Then you can integrate the 2nd contribution by parts and get
$$F'(u)=\int_{t_1}^{t_2} \mathrm{d}t \vec{\eta} \cdot \left [\frac{\partial L}{\partial \vec{x}} - \frac{\mathrm{d}}{\mathrm{d} t} \frac{\partial L}{ \partial \dot{\vec{x}}} \right].$$
Now set ##u## to 0 and then, because ##F'(u=0)=0## must hold for all trajectories ##\vec{\eta}(t)##, the bracket in the integral must vanish (this is known as the "fundamental theorem of variational calculus"), which leads to the Euler-Lagrange equations,
$$\frac{\mathrm{d}}{\mathrm{d} t} \frac{\partial L}{ \partial \dot{\vec{x}}}=\frac{\partial L}{\partial \vec{x}}.$$
This is the problem I have, I 100% understand this method(also the one from wikipedia), but the one my studyguide gives me I can't understand.
 
  • #6
Reuben_Leib said:
So from this I believe that I proved then necessary condition without the use of ##u## being small?
Sure? I don't follow your calculation in detail, but you set
$$
\dfrac{I'(0)}{2}\geq C\cdot |u| \geq \dfrac{I''(\varepsilon )}{2}|u|\text{ and so } \dfrac{I'(0)}{I''(\varepsilon }\geq |u|>0
$$
so you already assumed the size of ##u##.
 

1. What are Euler Lagrange equations?

Euler Lagrange equations are a set of differential equations used in the calculus of variations to find the extremum of a functional. They are derived from the Euler-Lagrange equation, which is a necessary condition for a curve to be an extremum of a functional.

2. How do I find neighboring curves of the extremum?

To find neighboring curves of the extremum, you can use the Euler Lagrange equations to solve for the extremal curve. Then, you can vary the extremal curve by adding a small perturbation to it and solve the Euler Lagrange equations again to find the neighboring curves.

3. What is the significance of neighboring curves of the extremum?

Neighboring curves of the extremum are important because they allow us to understand the behavior of the extremal curve and the functional around the extremum. They also help us to determine whether the extremum is a maximum, minimum, or a saddle point.

4. Can neighboring curves of the extremum be used to approximate the extremal curve?

Yes, neighboring curves of the extremum can be used to approximate the extremal curve. By varying the extremal curve and solving the Euler Lagrange equations, we can find a family of curves that converge to the extremal curve as the perturbation approaches zero.

5. Are there any practical applications of Euler Lagrange equations and neighboring curves of the extremum?

Yes, Euler Lagrange equations and neighboring curves of the extremum have many practical applications in various fields such as physics, engineering, and economics. They are used to solve optimization problems and to model the behavior of physical systems.

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