# Mollifier A mollifier (top) in dimension one. At the bottom, in red is a function with a corner (left) and sharp jump (right), and in blue is its mollified version.

In mathematics, mollifiers (also known as approximations to the identity) are smooth functions with special properties, used for example in distribution theory to create sequences of smooth functions approximating nonsmooth (generalized) functions, via convolution. Intuitively, given a function which is rather irregular, by convolving it with a mollifier the function gets "mollified", that is, its sharp features are smoothed, while still remaining close to the original nonsmooth (generalized) function. They are also known as Friedrichs mollifiers after Kurt Otto Friedrichs, who introduced them.

## Historical notes

Mollifiers were introduced by Kurt Otto Friedrichs in his paper Template:Harv, considered a watershed in the modern theory of partial differential equations. The name of this mathematical object had a curious genesis: Peter Lax tells the whole story of this genesis in his commentary Template:Harv. According to Lax, at that time, the mathematician Donald Alexander Flanders was a colleague of Friedrichs: since he liked to consult colleagues about English usage, he asked Flanders an advice on how to name the smoothing operator he was using. Flanders was a puritan, nicknamed by his friends Moll after Moll Flanders in recognition of his moral qualities: he suggested to call the new mathematical concept a "mollifier" as a pun incorporating both Flanders' nickname and the verb 'to mollify', meaning 'to smooth over' in a figurative sense.

Previously, Sergei Sobolev used mollifiers in his epoch making 1938 paper, which contains the proof of the Sobolev embedding theorem: Template:Harvtxt himself acknowledged Sobolev's work on mollifiers stating that:-"These mollifiers were introduced by Sobolev and the author...".

It must be pointed out that there is a little misunderstanding in the concept of mollifier: Friedrichs defined as "mollifier" the integral operator whose kernel is one of the functions nowadays called mollifiers. However, since the properties of a linear integral operator are completely determined by its kernel, the name mollfier was inherited by the kernel itself as a result of common usage.

## Definition

### Modern (distribution based) definition

Definition 1. If $\varphi$ is a smooth function on ℝn, n ≥ 1, satisfying the following three requirements

it is compactly supported
$\int _{{\mathbb {R} }^{n}}\!\varphi (x){\mathrm {d} }x=1$ $\lim _{\epsilon \to 0}\varphi _{\epsilon }(x)=\lim _{\epsilon \to 0}\epsilon ^{-n}\varphi (x/\epsilon )=\delta (x)$ where $\delta (x)$ is the Dirac delta function and the limit must be understood in the space of Schwartz distributions, then $\varphi$ is a mollifier. The function $\varphi$ could also satisfy further conditions: for example, if it satisfies

$\varphi$ $(x)$ ≥ 0 for all x ∈ ℝn, then it is called a positive mollifier
$\varphi$ $(x)$ =$\mu$ $(|x|)$ for some infinitely differentiable function $\mu$ : ℝ+ → ℝ, then it is called a symmetric mollifier

### Notes on Friedrichs' definition

Note 1. When the theory of distributions was still not widely known nor used, property Template:EquationNote above was formulated by saying that the convolution of the function $\varphi _{\epsilon }$ with a given function belonging to a proper Hilbert or Banach space converges as ε → 0 to this last one: this is exactly what Friedrichs did. This also clarifies why mollifiers are related to approximate identities.

Note 2. As briefly pointed out in the "Historical notes" section of this entry, originally, the term "mollifier" identified the following convolution operator:

$\Phi _{\epsilon }(f)(x)=\int _{{\mathbb {R} }^{n}}\varphi _{\epsilon }(x-y)f(y){\mathrm {d} }y$ where $\varphi _{\epsilon }(x)=\epsilon ^{-n}\varphi (x/\epsilon )$ and $\varphi$ is a smooth function satisfying the first three conditions stated above and one or more supplementary conditions as positivity and symmetry.

## Concrete example

It is easily seen that this function is infinitely differentiable, non analytic with vanishing derivative for |x| = 1. Divide this function by its integral over the whole space to get a function $\varphi$ of integral one, which can be used as mollifier as described above: it is also easy to see that $\varphi$ $(x)$ defines a positive and symmetric mollifier.

## Properties

All properties of a mollifier are related to its behaviour under the operation of convolution: we list the following ones, whose proofs can be found in every text on distribution theory.

### Smoothing property

$T_{\epsilon }=T\ast \varphi _{\epsilon }$ ### Approximation of identity

$\lim _{\epsilon \to 0}T_{\epsilon }=\lim _{\epsilon \to 0}T\ast \varphi _{\epsilon }=T\in D^{\prime }({\mathbb {R} }^{n})$ ### Support of convolution

${\mathrm {supp} }T_{\epsilon }={\mathrm {supp} }(T\ast \varphi _{\epsilon })\subset {\mathrm {supp} }T+{\mathrm {supp} }\varphi _{\epsilon }$ ## Applications

The basic applications of mollifiers is to prove properties valid for smooth functions also in nonsmooth situations:

### Product of distributions

In some theories of generalized functions, mollifiers are used to define the multiplication of distributions: precisely, given two distributions $S$ and $T$ , the limit of the product of a smooth function and a distribution

$\lim _{\epsilon \to 0}S_{\epsilon }\cdot T=\lim _{\epsilon \to 0}S\cdot T_{\epsilon }{\overset {\mathrm {def} }{=}}S\cdot T$ defines (if it exists) their product in various theories of generalized functions.

### "Weak=Strong" theorems

Very informally, mollifiers are used to prove the identity of two different kind of extension of differential operators: the strong extension and the weak extension. The paper Template:Harv illustrates this concept quite well: however the high number of technical details needed to show what this really means prevent them from being formally detailed in this short description.

### Smooth cutoff functions

$\chi _{B_{1},1/2}(x)=\chi _{B_{1}}\ast \varphi _{1/2}(x)=\int _{{\mathbb {R} }^{n}}\!\!\!\chi _{B_{1}}(x-y)\varphi _{1/2}(y){\mathrm {d} }y=\int _{B_{1/2}}\!\!\!\chi _{B_{1}}(x-y)\varphi _{1/2}(y){\mathrm {d} }y\ \ \ (\because supp(\varphi _{1/2})=B_{1/2})$ $\int _{B_{1/2}}\!\!\!\chi _{B_{1}}(x-y)\varphi _{1/2}(y){\mathrm {d} }y=\int _{B_{1/2}}\!\!\!\varphi _{1/2}(y){\mathrm {d} }y=1$ .

It is easy to see how this construction can be generalized to obtain a smooth function identical to one on a neighbourhood of a given compact set, and equal to zero in every point whose distance from this set is greater than a given $\epsilon$ . Such a function is called a (smooth) cutoff function: those functions are used to eliminate singularities of a given (generalized) function by multiplication. They leave unchanged the value of the (generalized) function they multiply only on a given set, thus modifying its support: also cutoff functions are the basic parts of smooth partitions of unity.