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Derivative of a Mollified Lipschitz Map

Proved
FunctionalInequalities.Analysis.hasFDerivAt_convolution_normed_of_lipschitz_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This establishes the derivative formula for convolution with a normalized smooth bump.

Let EEE be a finite-dimensional real inner-product space with its Borel structure. Let F:E→EF:E\to EF:E→E be Lipschitz with Lipschitz bound G∈[0,∞)G\in[0,\infty)G∈[0,∞), let φ\varphiφ be a smooth compactly supported bump centered at 000, and let x∈Ex\in Ex∈E. Then the convolution of FFF with the normalized bump φnorm\varphi^{\mathrm{norm}}φnorm has a Fréchet derivative at xxx given by the convolution of the almost-everywhere derivative of FFF:

D(φnorm ⁣∗F)(x)=(φnorm ⁣∗DF)(x).D\bigl(\varphi^{\mathrm{norm}}\!\ast F\bigr)(x) = \bigl(\varphi^{\mathrm{norm}}\!\ast DF\bigr)(x).D(φnorm∗F)(x)=(φnorm∗DF)(x).

In Lean, the convolution uses ContinuousLinearMap.lsmul, volume measure, and fderiv ℝ F. This is a supporting result for mollification identities.

Original Lean declaration header

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

theorem hasFDerivAt_convolution_normed_of_lipschitz
    {F : E → E} {G : NNReal} (hF : LipschitzWith G F) (φ : ContDiffBump (0 : E)) (x : E) :
    HasFDerivAt (φ.normed (volume : Measure E) ⋆[ContinuousLinearMap.lsmul ℝ ℝ, (volume : Measure E)] F)
      ((φ.normed (volume : Measure E) ⋆[ContinuousLinearMap.lsmul ℝ ℝ, (volume : Measure E)]
        fderiv ℝ F) x) x

Module navigation

Module: Analysis/Mollification/Basic.

Supporting theorems: integral_fderiv_kernel_eq.

Previous: integral_fderiv_kernel_eq.

Source excerpt, lines 516–520 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Mollification_Basic

open MeasureTheory
open scoped Convolution
variable {E : Type*}
variable [NormedAddCommGroup E] [InnerProductSpace ℝ E] [FiniteDimensional ℝ E] [MeasurableSpace E]
variable [BorelSpace E]
Formal statement

theorem FunctionalInequalities.Analysis.hasFDerivAt_convolution_normed_of_lipschitz_readable
    {F : E → E} {G : NNReal} (hF : LipschitzWith G F) (φ : ContDiffBump (0 : E)) (x : E) :
    HasFDerivAt (φ.normed (volume : Measure E) ⋆[ContinuousLinearMap.lsmul ℝ ℝ, (volume : Measure E)] F)
      ((φ.normed (volume : Measure E) ⋆[ContinuousLinearMap.lsmul ℝ ℝ, (volume : Measure E)]
        fderiv ℝ F) x) x := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Mollification/Basic.lean#L512-L557 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Mollification.Basic`, declaration `FunctionalInequalities.Analysis.hasFDerivAt_convolution_normed_of_lipschitz`; the repository is private at upload time).

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