# Introduction to Mathematical Physics/Dual of a topological space

## Contents

## Definition[edit]

**Definition:**

Let be a topological vectorial space. The set of the continuous linear form on is a vectorial subspace of and is called the topological dual of .

### Distributions[edit]

chapdistr

Distributions\index{distribution} allow to describe in an elegant and synthetic way lots of physical phenomena. They allow to describe charge "distributions" in electrostatic (like point charge, dipole charge). They also allow to genereralize the derivation notion to functions that are not continuous.

**Definition:**

L. Schwartz distributions are linear functionals continuous on , thus the elements of the *dual* of .

**Definition:**

A function is called locally summable if it is integrable in Lebesgue sense over any bounded interval.

**Definition:**

To any locally summable function , a distribution defined by:

can be associated.

**Definition:**

Dirac distribution,\index{Dirac distribution} noted is defined by:

**Remark:**

Physicist often uses the (incorrect!) integral notation:

to describe the action of Dirac distribution on a function .

*Convolution* of two functions and is the function if exists defined by:

and is noted:

Convolution product of two distributions and is (if exists) a distribution noted defined by:

Here are some results:

- convolution by is unity of convolution.
- convolution by is the derivation.
- convolution by is the derivation of order .
- convolution by is the translation of .

The notion of *Fourier transform* of functions can be extended to distributions. Let us first recall the definition of the Fourier transform of a function:

**Definition:**

Let be a complex valuated function\index{Fourier transform} of the real variable . Fourier transform of is the complex valuated function \sigma</math> defined by:

if it exists.

A sufficient condition for the Fourier transform to exist is that is summable. The Fourier transform can be inverted: if

then

Here are some useful formulas:

Let us now generalize the notion of Fourier transform to distributions. The Fourier transform of a distribution can not be defined by

Indeed, if , then and the second member of previous equality does not exist.

**Definition:**

Space is the space of fast decreasing functions. More precisely, if

- its derivative exists for any positive integer .
- for all positive or zero integers and , is bounded.

**Definition:**

Fourier transform of a tempered distribution is the distribution defined by

The Fourier transform of the Dirac distribution is one:

Distributions\index{distribution} allow to describe in an elegant and synthetic way lots of physical phenomena. They allow to describe charge "distributions" in electrostatic (like point charge, dipole charge). They also allow to genereralize the derivation notion to functions that are not continuous.

## Statistical description[edit]

### Random variables[edit]

Distribution theory generalizes the function notion to describe\index{random variable} physical objects very common in physics (point charge, discontinuity surfaces,\dots). A random variable describes also very common objects of physics. As we will see, distributions can help to describe random variables. At section secstoch, we will introduce stochastic processes which are the mathemarical characteristics of being nowhere differentiable.

Let a tribe of parts of a set of "results" . An event is an element of , that is a set of 's. A probability is a positive measure of tribe . The faces of a dice numbered from 0 to 6 can be considered as the results of a set . A random variable is an application from into (or ). For instance one can associate to each result of the de experiment a number equal to the number written on the face. This number is a random variable.

### Probability density[edit]

Distribution theory provides the right framework to describe statistical "distributions". Let be a random variable that takes values in .

**Definition:**

The density probability function is such that:

It satisfies:

**Example:**

The density probability function of a Bernoulli process is:

### Moments of the partition function[edit]

Often, a function is described by its moments:

**Definition:**

The moment of function is the integral

**Definition:**

The mean of the random variable or mathematical expectation is moment

**Definition:**

Variance is the second order moment:

The square root of variance is called ecart-type and is noted .

### Generating function[edit]

**Definition:**

The generatrice function\index{generating function} of probability density is the Fourier transform of.

**Example:**

For the Bernouilli distribution:

The property of Fourier transform:

implies that:

## Sum of random variables[edit]

**Theorem:**

The density probability associated to the sum of two {\bf independent} random variables is the convolution product of the probability densities and .

We do not provide here a proof of this theorem, but the reader can on the following example understand how convolution appears. The probability that the sum of two random variables that can take values in with is is, taking into account all the possible cases:

This can be used to show the probability density associated to a binomial law. Using the Fourier counterpart of previous theorem:

So

Let us state the central limit theorem.

**Theorem:**

The convolution product of a great number of functions tends\footnote{ The notion of limit used here is not explicited, because this result will not be further used in this book. } to a Gaussian. \index{central limit theorem}

**Proof:**

Let us give a quick and dirty proof of this theorem. Assume that has the following Taylor expansion around zero:

and that the moments with are zero. then using the definition of moments:

This implies using that:

A Taylor expansion yields to

Finally, inverting the Fourier transform: