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Mollification Commutes with Weak Directional Differentiation

Proved
FunctionalInequalities.Analysis.fderiv_convolution_eq_of_hasWeakDerivAlong_readable

by Lazyfill · Oct 26, 2026 · Mathlib 0df444a (Lean v4.33.1)

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This is the generic weak-derivative identity for mollification.

Let EEE be a finite-dimensional real inner-product space with its Borel measurable structure. Let f,g:E→Rf,g:E\to\mathbb{R}f,g:E→R and v∈Ev\in Ev∈E, and assume HasWeakDerivAlong f g v. Let φ\varphiφ be a smooth compactly supported bump centered at 000, and let x∈Ex\in Ex∈E. Then

Dv((φnormed∗f)(x))=(φnormed∗g)(x).D_v\bigl((\varphi_{\mathrm{normed}}\ast f)(x)\bigr) =(\varphi_{\mathrm{normed}}\ast g)(x).Dv​((φnormed​∗f)(x))=(φnormed​∗g)(x).

In Lean, the directional derivative is fderiv ℝ (...) x v, and the convolution uses volume measure. This identifies the classical derivative of the mollified function with the mollification of the weak derivative.

Original Lean declaration header

Within namespace FunctionalInequalities.Analysis. Shown in its original module context. Proof omitted.

theorem fderiv_convolution_eq_of_hasWeakDerivAlong {E : Type*} [NormedAddCommGroup E]
    [InnerProductSpace ℝ E] [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {f g : E → ℝ} {v : E} (h : HasWeakDerivAlong f g v) (φ : ContDiffBump (0 : E)) (x : E) :
    fderiv ℝ (convolution (φ.normed volume) f (ContinuousLinearMap.lsmul ℝ ℝ) volume) x v =
      convolution (φ.normed volume) g (ContinuousLinearMap.lsmul ℝ ℝ) volume x

Module navigation

Module: Analysis/WeakDerivative/Mollification.

Definition modules: Analysis/WeakDerivative/Basic.

Next: quotient_pairing_tendsto.

Source excerpt, lines 25–29 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_WeakDerivative_Mollification

open MeasureTheory Set Filter
Formal statement

theorem FunctionalInequalities.Analysis.fderiv_convolution_eq_of_hasWeakDerivAlong_readable {E : Type*} [NormedAddCommGroup E]
    [InnerProductSpace ℝ E] [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {f g : E → ℝ} {v : E} (h : HasWeakDerivAlong f g v) (φ : ContDiffBump (0 : E)) (x : E) :
    fderiv ℝ (convolution (φ.normed volume) f (ContinuousLinearMap.lsmul ℝ ℝ) volume) x v =
      convolution (φ.normed volume) g (ContinuousLinearMap.lsmul ℝ ℝ) volume x := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/WeakDerivative/Mollification.lean#L23-L83 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.WeakDerivative.Mollification`, declaration `FunctionalInequalities.Analysis.fderiv_convolution_eq_of_hasWeakDerivAlong`; the repository is private at upload time).

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