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Mollifier

Mollifier is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Mollifier rather than just read about it. In short: In mathematics, mollifiers (also known as approximations to the identity) are particular smooth functions, used for example in distribution theory to create sequences of smooth functions approximating nonsmooth (generalized) functions, via convolution. Intuitively, given a (generalized) function, convolving it with a mollifier "mollifies" it, that is, its sharp features are smoothed, while still remaining close to t…

Mollifier — main illustration
Mollifier — illustration

Key takeaways

  • Mollifier belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Mollifier to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Mollifier from memory before moving on to harder problems.

Reference excerpt

In mathematics, mollifiers (also known as approximations to the identity) are particular smooth functions, used for example in distribution theory to create sequences of smooth functions approximating nonsmooth (generalized) functions, via convolution. Intuitively, given a (generalized) function, convolving it with a mollifier "mollifies" it, that is, its sharp features are smoothed, while still remaining close to the original. 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 (Friedrichs 1944, pp. 136–139), which is considered a watershed in the modern theory of partial differential equations. The name of this mathematical object has a curious genesis, and Peter Lax tells the story in his commentary on that paper published in Friedrichs' "Selecta". According to him, 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 for advice on naming the smoothing operator he was using. Flanders was a modern-day puritan, nicknamed by his friends Moll after Moll Flanders in recognition of his moral qualities: he suggested calling 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 had used mollifiers in his epoch making 1938 paper, which contains the proof of the Sobolev embedding theorem: Friedrichs himself acknowledged Sobolev's work on mollifiers, stating "These mollifiers were introduced by Sobolev and the author...". It must be pointed out that the term "mollifier" has undergone linguistic drift since the time of these foundational works: 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 mollifier was inherited by the kernel itself as a result of common usage.

Definition

Modern (distribution based) definition Definition 1. Let φ {\displaystyle \varphi } be a smooth function on R n {\displaystyle \mathbb {R} ^{n}} , n ≥ 1 {\displaystyle n\geq 1} , and put φ ϵ ( x ) := ϵ − n φ ( x / ϵ ) {\displaystyle \varphi _{\epsilon }(x):=\epsilon ^{-n}\varphi (x/\epsilon )} for ϵ > 0 ∈ R {\displaystyle \epsilon >0\in \mathbb {R} } . Then φ {\displaystyle \varphi } is a mollifier if it satisfies the following three requirements:

(1) it is compactly supported, (2) ∫ R n φ ( x ) d x = 1 {\displaystyle \int _{\mathbb {R} ^{n}}\!\varphi (x)\mathrm {d} x=1} , (3) lim ϵ → 0 φ ϵ ( x ) = lim ϵ → 0 ϵ − n φ ( x / ϵ ) = δ ( x ) {\displaystyle \lim _{\epsilon \to 0}\varphi _{\epsilon }(x)=\lim _{\epsilon \to 0}\epsilon ^{-n}\varphi (x/\epsilon )=\delta (x)} , where δ ( x ) {\displaystyle \delta (x)} is the Dirac delta function, and the limit must be understood as taking place in the space of Schwartz distributions. The function φ {\displaystyle \varphi } may also satisfy further conditions of interest; for example, if it satisfies

(4) φ ( x ) ≥ 0 {\displaystyle \varphi (x)\geq 0} for all x ∈ R n {\displaystyle x\in \mathbb {R} ^{n}} , then it is called a positive mollifier, and if it satisfies

(5) φ ( x ) = μ ( | x | ) {\displaystyle \varphi (x)=\mu (|x|)} for some infinitely differentiable function μ : R + → R {\displaystyle \mu :\mathbb {R} ^{+}\to \mathbb {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 (3) above was formulated by saying that the convolution of the function φ ϵ {\displaystyle \scriptstyle \varphi _{\epsilon }} with a given function belonging to a proper Hilbert or Banach space converges as ε → 0 to that function: 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:

… excerpt ends here. Continue reading the full article.

Illustrations

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.
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.
Mollifier: A function undergoing progressive mollification.
A function undergoing progressive mollification.
Mollifier: The function 
  
    
      
        φ
      
    
    {\displaystyle \varphi }
  

  
    
      
        (
        x
        )
      
    
    {\displaystyle (x)}
  
 in dimension one
The function φ {\displaystyle \varphi } ( x ) {\displaystyle (x)} in dimension one

Worked examples

Example 1 — a first encounter with Mollifier

Start with the simplest possible case. Write down what Mollifier claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Mollifier before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Mollifier ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Mollifier

In research
Mollifier appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Mollifier in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Mollifier is common in secondary-school and first-year university syllabi. It links to neighbouring topics Functional analysis, Schwartz distributions, Smooth functions, so understanding it makes those chapters shorter.
In everyday life
Look for Mollifier outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Mollifier in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Mollifier means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Mollifier out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Mollifier in simple terms?

In mathematics, mollifiers (also known as approximations to the identity) are particular smooth functions, used for example in distribution theory to create sequences of smooth functions approximating nonsmooth (generalized) functions, via convolution. Intuitively, given a (generalized) function, c…

Why does Mollifier matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Mollifier?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Mollifier.

Tags

  • Functional analysis
  • Schwartz distributions
  • Smooth functions

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