Question about derivation of Euler-Lagrange eq.

In summary, the necessary condition for the action, S, to have a minimum is that the first variation of the integral must be zero. This means that small variations in the path near the minimum of the action will not change the value of the action. This is similar to how the derivative of a function must be zero at its minimum point in calculus. The idea is the same in the 3-dimensional case of minimizing the action. In short, "the first order variation in S (action) has to be zero for S to be at a minimum" is just another way of saying "the derivative of S is zero at a minimum".
  • #1
Agrasin
69
2
I've attached the part from Landau & Lifschitz Mechanics where I got confused.

"The necessary condition for S(action) to have a minimum (extremum) is that these terms (called the first variation, or simply the variation, of the integral) should be zero. "

Why is this a necessary condition? If you could point me towards a definition AND explain intuitively that'd be great.

(Background: I've learned calculus and Taylor expansions-- I think this is related. But, visually, I don't see why a variation in the function at the minimum should be zero. Imagine a paraboloid. The bottom is the minimum. If you go a tiny bit in any direction (a variation) then the first-order change in the function is NOT zero. Maybe it's just that I don't understand what first-order change means.)
 

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  • #2
It's just saying that if it is a minimum then it has to be at the bottom of a bowl, so to speak. Just as the ordinary derivative of a function has to be zero at the function's minimum, so to does the action have to be unchanged by small variations in the path near the minimum of the action.
 
  • #3
"Just as the ordinary derivative of a function has to be zero at the function's minimum, so to does the action have to be unchanged by small variations in the path near the minimum of the action."

Okay, please explain this analogy. I agree that the derivative of a function is zero at a minimum. But f(x+h) for any small variation h is NOT equal to f(x) necessarily. In fact, at a minimum, f(x+h) > f(x).
 
  • #4
For simplicity, let's just deal with the 2-dimensional case. It's true that at the minimum, whichever way you go, you will always 'go up the hill' (so to speak). Hence, it's obvious that [itex] f(x-h) >f(x)[/itex]; and [itex] f(x+h) >f(x)[/itex] (for any small variation [itex]h[/itex]). So visually, the graph decreases as it goes from [itex]x-h[/itex] to [itex] x[/itex], and it increases as it goes from [itex]x[/itex] to [itex] x+h[/itex]. In calculus, when a function decreases, it'll have a negative slope, which means that its derivative is negative. Similarly, when a function increases, it'll have a positive slope, which means that its derivative is positive. And since the derivative changes sign (from negative to positive) at the minimum point as the function goes from [itex] x-h[/itex] to [itex]x + h[/itex], the derivative must be zero at this minimum point. All of this is the basic idea behind the first derivative test in calculus. So I would suggest you to refresh your mind a bit on calculus 101 because that will probably clear up all your confusion.

Once you understand what I've said above, then you can easily apply it to the case of minimizing the action in 3-dimensional coordinate. The idea is just the same!
 
  • #5
So "the first order variation in S (action) has to be zero for S to be at a minimum" is just a fancy way of saying "the derivative of S is zero at a minimum"?
 
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  • #6
Agrasin said:
So "the first order variation in S (action) has to be zero for S to be at a minimum" is just a fancy way of saying "the derivative of S is zero at a minimum"?

Well, I think you got it. You can re-write the quote by just replacing the word 'variation' with the word 'derivative' (because they mean basically the same thing as far as differentiation is concerned). In other words: "the first-order [derivative] in S (action) has to be zero for S to be at a minimum". It is totally not a fancy way of saying things, because it describes exactly what a minimum is.
 
  • #7
It may help to read a little about functional derivatives: http://www.physics.byu.edu/faculty/berrondo/wt752/functional%20derivative.pdf
 
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What is the Euler-Lagrange equation?

The Euler-Lagrange equation is a fundamental equation in the field of calculus of variations. It is used to find the function that minimizes or maximizes a given functional, which is a mathematical expression involving a function and its derivatives.

What is the purpose of the Euler-Lagrange equation?

The Euler-Lagrange equation is used to find the optimal path or function that minimizes or maximizes a certain quantity, such as time, energy, or cost. It is widely used in physics, engineering, economics, and other fields to solve optimization problems.

How is the Euler-Lagrange equation derived?

The Euler-Lagrange equation is derived by setting the variation of the functional to zero and applying the calculus of variations. This leads to a differential equation that the optimal function must satisfy, which is the Euler-Lagrange equation.

What is the relationship between the Euler-Lagrange equation and the principle of least action?

The Euler-Lagrange equation is closely related to the principle of least action in physics. The principle of least action states that the actual path of a physical system is the one that minimizes the action, which is defined as the integral of the Lagrangian over time. This is equivalent to solving the Euler-Lagrange equation for the system.

What are the applications of the Euler-Lagrange equation?

The Euler-Lagrange equation has many applications in physics, engineering, economics, and other fields. It is used to solve optimization problems, such as finding the shortest or fastest path, minimizing energy consumption, or maximizing profit. It is also used in the development of mathematical models and in the study of dynamical systems.

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