# Variational derivative

Zadorozhny's now reading lectures on what he calls "variational methods and
random processes". Despite of some common abuse of notation the course seems to
be qute useful. I'll post some summaries of what he's teaching us. Yet I'll use
slightly different notation and thus as always, all the good is his and all the
wrong is mine. **This is the part about variational derivatives**.

### Spaces

Let be one of the following Banach spaces:

- the space of bounded continuous functions defined on the interval with the norm:

- the space of functions possessing bounded continuous derivatives:

- the space of classes of equivalent functions with the norm:

### Definition

We consider a functional and we consider it the context
of variational problems or optimal control problems. The common approach to
investigate these problems is linearization. Now we could quite naturally begin
with consideration of Frechet derivative of (assuming it has one):
where is a bounded linear operator and is the Frechet derivative of
at the function . Yet it turns out there's something even more
convenient. As we saw earlier^{1}^{2} the functional is often
an integral one. This and some further exploration leads to the following
definition:

The function is called a *functional*
or *variational derivative* of if the Frechet derivative at any
function is a *bounded integral operator* with the *kernel*

In simpler words, the variational derivative is a kernel of the Frechet derivative.

**Nota Bene:** while the Frechet derivative is unique it may have infinitely
many equivalent *kernels*.

### Notation

There's an established notation for the variational derivative. As you could've
guessed it's a bit messy. Although aware of the distinction, in his works
Zadorozhny usually doesn't distinguish a function from its value a derivative
from the increment of the value associated with specific increment of the
argument --- that is the derivative applied to that increment. The wikipedia
page^{3} is prone to a similar abuse of notation.

Thus so far we will use the following notation for *the value of the functional
derivative at specific function and time :*

Sadly, Zadorozhny often calls this thing a function which of course it isn't.
He also uses this same symbol for the value of the derivative and for all of the
functions
,
,
It's common though. The real problem comes from the fact that he also calls both
a *derivative* and a *differential* (and not just their value) the following
expression:

I suppose that's because he's talking too much with physics folks. The common
notion of a derivative is that of a *linear mapping* which approximates the
function under consideration as good as possible. The integral above isn't a
derivative of but the function
is.
The notion of a "differential" is then a non-sense. As
opposed to the notion of a "differential associated with specific change ".
Which is still excessive. That's a long and old story though which probably
deserves its own writeup.

To denote the function itself I'd rather propose the notation

Yet obviously things are going to get more complicated than that when it comes to multivaried functionals and the change of variables. In these cases I believe multiindex notation could be used just as with usual derivatives.

### Examples

### Inclusions

It is important to note that spaces are included in each other as normed spaces so that if is a bounded linear operator in then it is just as well bounded in all . And so are .

Suppose we have two norms which lie in the following relation:

Then

Thus whenever

it's also

In other words if as then as .

Now it's easily seen that

and^{4}

which proves our assertion about inclusions.

### Properties or "The rules of variational differentiation"

Here one needs to be precise in what spaces the derivative is bounded and where it's not. Despite of that I'll omit proofs for some trivial cases.

#### Derivative of a constant functional

#### Homogeneity

If has a variational derivative, then so does and:

#### Additivity

If the functionals and have variational derivatives, then so does their sum and the following equality holds:

#### Derivative of a multiple

If the functionals and have variational derivatives then so does and:

That is .

#### Chain rule 1

Suppose has a variational derivative and is a function. Consider a functional

Then

##### Proof

#### Chain rule 2

Suppose has a variational derivative and is a function. Consider a functional

If is , then has a variational derivative and:

Just in case you're not familiar with this notation, let's rewrite this definition using an auxilliary function defined by when we fix some . Then .

Then the statement above can be rewritten as:

##### Proof

Let's consider an increment:

In the second line we used a linear approximation of the value of a differentiable function .

In the third line we've rewritten the preceding expression so that we get the expression of the form , where and are two functions.

This allows us to use a linear approximation in the fourth line.

Note the in the end. It's actually .

It follows then that in (and in by inclusion) the variational derivative of does exist and:

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