# Deriving Euler-Lagrange Equation

I have just started studying Calculus of Variations, and need some help about deriving the Euler-Lagrange equation.

In the book I'm reading, the writer starts by imposing the following inner product on the infinite dimensional function space: $$\langle f;g\rangle=\int_a^bf(x).g(x)dx$$Then he states what he called "the basic directional derivative formula" $$\langle\nabla J[u];v\rangle=\frac{d}{d\varepsilon}J[u+\varepsilon v]$$ at $\varepsilon=0$ and where $J$ is the objective functional.

The minimizing function of $J$ is a one satisfying $\langle \nabla J[u];v\rangle=0$

The writer then works for a formula of $\langle\nabla J[u];v\rangle$ and finally reaches :$$\langle\nabla J[u];v\rangle=\int_a^bv.[\dfrac{\partial L}{\partial u}-\dfrac{d}{dx}(\dfrac{\partial L}{\partial u'})]dx +\dfrac{\partial L}{\partial u'}(b).v(b)-\dfrac{\partial L}{\partial u'}(a)v(a)$$Then states that, for suspect functions (those verifing boundary conditions), $v(a)=v(b)=0$, and finally deduces Euler-Lagrange equation for the minmizing function :$$\dfrac{\partial L}{\partial u}-\dfrac{d}{dx}(\dfrac{\partial L}{\partial u'})=0$$

I have two questions now:

1. Shouldn't the norm of $v$ be equal to 1 in order to satisfy the formula of directional derivative?

2. Shouldn't we prove that the region of suspect functions is connected so that we can talk about a directional derivative at a function in the direction of another?

Please help.

## 1 Answer

1) In principle you are right, and in the definition of $\nabla J$ there should be a division by $\| v \|$ in order to mimick the usual formula from calculus on $\Bbb R ^n$. Nevertheless, keep in mind that you are interested in the solutions of $\nabla J = 0$, and these do not care about the equation being divided by non-zero numbers.

There is a deeper reason, though, for $\| v \|$ not being present in the denominator: in a normed space, you may speak about the Fréchet derivative. In a space without norm, though, what can you do? The concept to use is the Gâteaux derivative, which is exactly the one used in defining $\nabla J$. It happens that you have a norm on your space, but the definition given works on spaces without norm, too; it wouldn't have worked if there had been that $\| v \|$ in the denominator (what would you have replaced it with in the absence of a norm?).

2) I don't see why you require connectedness, but if you really want it then note that the set of functions that vanish on the boundary is even better - it is pathwise connected: for every $f, g$ in it, the segment $tf + (1-t)g$ vanishes on the boundary too. Anyway, you don't need this (think about derivability in $\Bbb R ^n$: where do you use connectedness? nowhere).

• Thanks for answering my questions. In the second question I wasn't asking about connectedness of variations $v$, but rather the functions $u$ from which we have to pick the minimizing function, those need to satisfy some boundary conditions but not necessarily vanish at the boundaries. I thought that if these functions weren't connected (were singular distinct points for example), then it wouldn't be possible to have a directional derivative at one function in the direction of another. – Tofi Mar 31 '16 at 16:06
• @H.Tofaili: Wait, you're confusing me: what do you mean by "connectedness of a function"? Such a concept doesn't even exist! – Alex M. Mar 31 '16 at 16:17
• Connectedness doesn't matter for differentiation, you only need your underlying set to be open. – Alex M. Mar 31 '16 at 17:04
• I had to say : connectedness of the domain {$u$/$u$ satisfies boundary conditions}. – Tofi Mar 31 '16 at 17:06
• Connectedness DOES NOT MATTER! – Alex M. Mar 31 '16 at 17:07