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Weak Derivative of a Composed Coordinate Derivative

Proved
FunctionalInequalities.Analysis.exists_lipschitzWith_weak_fderiv_comp_gradient_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This is a supporting weak-Hessian statement for the second evolution identity of Letwin (arXiv:2607.24164v1), equation (2.6).

Let u,W:Rn→Ru,W:\mathbb R^n\to\mathbb Ru,W:Rn→R. Assume u∈C1u\in C^1u∈C1, uuu is globally Lipschitz, ∇u\nabla u∇u is globally Lipschitz, and W∈C2W\in C^2W∈C2. For each a∈Fin na\in\mathrm{Fin}\ na∈Fin n, there exists C∈[0,∞)C\in[0,\infty)C∈[0,∞) such that x↦DW(∇u(x))[ea]x\mapsto DW(\nabla u(x))[e_a]x↦DW(∇u(x))[ea​] is CCC-Lipschitz. For every jjj, it has a weak derivative along eje_jej​ given by

∑bHu(x)jb HW(∇u(x))ba.\sum_b H_u(x)_{jb}\,H_W(\nabla u(x))_{ba}.b∑​Hu​(x)jb​HW​(∇u(x))ba​.

This derivative belongs to L2L^2L2 on every compact set. No global L2L^2L2 estimate or inverse-Hessian bound is asserted.

Original Lean declaration header

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

theorem exists_lipschitzWith_weak_fderiv_comp_gradient {n : ℕ}
    {u W : EuclideanSpace ℝ (Fin n) → ℝ} {L G : NNReal}
    (hu : ContDiff ℝ 1 u) (hULip : LipschitzWith L u)
    (hGradLip : LipschitzWith G (gradient u)) (hW : ContDiff ℝ 2 W)
    (a : Fin n) :
    ∃ C : NNReal,
      LipschitzWith C (fun x => fderiv ℝ W (gradient u x)
        (EuclideanSpace.basisFun (Fin n) ℝ a)) ∧
      ∀ j : Fin n,
        HasWeakDerivAlong
          (fun x => fderiv ℝ W (gradient u x) (EuclideanSpace.basisFun (Fin n) ℝ a))
          (fun x => ∑ b : Fin n, hessianMatrix u x j b * hessianMatrix W (gradient u x) b a)
          (EuclideanSpace.basisFun (Fin n) ℝ j) ∧
        ∀ S : Set (EuclideanSpace ℝ (Fin n)), IsCompact S →
          MemLp
            (fun x => ∑ b : Fin n, hessianMatrix u x j b * hessianMatrix W (gradient u x) b a)
            2 ((volume : Measure (EuclideanSpace ℝ (Fin n))).restrict S)

Module navigation

Module: Analysis/BakryEmery/IntegrationByParts/WeakHessianEvolution.

Definition modules: Analysis/BakryEmery/HessianOperator, Analysis/WeakDerivative/Basic.

Supporting theorems: HasWeakDerivAlong.congr_ae, ae_fderiv_mongeAmpere_exponent, exists_lipschitzWith_neg_add_comp_gradient, hasWeakDerivAlong_of_lipschitz.

Next: hasWeakDerivAlong_mongeAmpere_first_rhs.

Source excerpt, lines 86–102 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_BakryEmery_IntegrationByParts_WeakHessianEvolution

open MeasureTheory Set
open FunctionalInequalities.Probability.Stein
set_option maxHeartbeats 800000
Formal statement

theorem FunctionalInequalities.Analysis.exists_lipschitzWith_weak_fderiv_comp_gradient_readable {n : ℕ}
    {u W : EuclideanSpace ℝ (Fin n) → ℝ} {L G : NNReal}
    (hu : ContDiff ℝ 1 u) (hULip : LipschitzWith L u)
    (hGradLip : LipschitzWith G (gradient u)) (hW : ContDiff ℝ 2 W)
    (a : Fin n) :
    ∃ C : NNReal,
      LipschitzWith C (fun x => fderiv ℝ W (gradient u x)
        (EuclideanSpace.basisFun (Fin n) ℝ a)) ∧
      ∀ j : Fin n,
        HasWeakDerivAlong
          (fun x => fderiv ℝ W (gradient u x) (EuclideanSpace.basisFun (Fin n) ℝ a))
          (fun x => ∑ b : Fin n, hessianMatrix u x j b * hessianMatrix W (gradient u x) b a)
          (EuclideanSpace.basisFun (Fin n) ℝ j) ∧
        ∀ S : Set (EuclideanSpace ℝ (Fin n)), IsCompact S →
          MemLp
            (fun x => ∑ b : Fin n, hessianMatrix u x j b * hessianMatrix W (gradient u x) b a)
            2 ((volume : Measure (EuclideanSpace ℝ (Fin n))).restrict S) := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/BakryEmery/IntegrationByParts/WeakHessianEvolution.lean#L83-L249 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.BakryEmery.IntegrationByParts.WeakHessianEvolution`, declaration `FunctionalInequalities.Analysis.exists_lipschitzWith_weak_fderiv_comp_gradient`; the repository is private at upload time).

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