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Leibniz Rule For The Closed Weighted Gradient

Proved
FunctionalInequalities.KLS.LogConcave.exists_mem_domain_closedWeightedOperator_toLp_mul_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This theorem extends the product rule to every element of the closed weighted-gradient domain.

Let VVV be finite-dimensional with the stated structures, let mmm be an additive Haar measure, let ν\nuν be log-concave with nonempty interior support, and let B:V→VB:V\to VB:V→V be continuous linear. For a smooth compactly supported multiplier ϕ:V→R\phi:V\to\mathbb Rϕ:V→R and any

x∈D ⁣(T‾ν,m,B),x\in D\!\left(\overline{T}_{\nu,m,B}\right),x∈D(Tν,m,B​),

there exists a proof of domain membership for the L2(ν)L^2(\nu)L2(ν) class of ϕx\phi xϕx, and the resulting operator value satisfies, ν\nuν-almost everywhere,

T‾ν,m,B(ϕx)=ϕ T‾ν,m,Bx+x (B∇ϕ).\overline{T}_{\nu,m,B}(\phi x) = \phi\,\overline{T}_{\nu,m,B}x+x\,(B\nabla\phi).Tν,m,B​(ϕx)=ϕTν,m,B​x+x(B∇ϕ).

The statement uses toLp for the product class and closedWeightedOperator for the closed operator. No boundedness or differentiability of xxx is assumed.

Original Lean declaration header

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

theorem exists_mem_domain_closedWeightedOperator_toLp_mul {φ : V → ℝ} (hφ : ContDiff ℝ ∞ φ)
    (hφc : HasCompactSupport φ) (x : (hν.closedWeightedOperator m B hi).domain) :
    ∃ hmem : (memLp_mul_of_contDiff_hasCompactSupport (ν := ν) hφ hφc
        (x : Lp ℝ 2 (ν : Measure V))).toLp _ ∈ (hν.closedWeightedOperator m B hi).domain,
      ((hν.closedWeightedOperator m B hi ⟨_, hmem⟩ : Lp V 2 (ν : Measure V)) : V → V)
        =ᵐ[(ν : Measure V)] fun y =>
          φ y • ((hν.closedWeightedOperator m B hi x : Lp V 2 (ν : Measure V)) : V → V) y
            + (x : Lp ℝ 2 (ν : Measure V)) y • apply B φ y

Module navigation

Module: KLS/LogConcave/WeightedGradient/Multiplier.

Definition modules: KLS/LogConcave/WeightedGradient/Closure, Probability/LogConcave, Energy/WeightedGradient/Basic.

Supporting theorems: exists_seq_smooth_tendsto_graph.

Previous: exists_seq_smooth_tendsto_graph.

Source excerpt, lines 134–141 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_KLS_LogConcave_WeightedGradient_Multiplier

open MeasureTheory Filter Topology
open scoped ContDiff
open FunctionalInequalities.Energy FunctionalInequalities.Energy.WeightedGradient FunctionalInequalities.Analysis.Semigroup
variable {V : Type*} [NormedAddCommGroup V] [InnerProductSpace ℝ V] [CompleteSpace V]
  [MeasurableSpace V] [BorelSpace V] [SecondCountableTopology V] [FiniteDimensional ℝ V]
variable {ν : MeasureTheory.ProbabilityMeasure V} (m : MeasureTheory.Measure V) [m.IsAddHaarMeasure] (B : V →L[ℝ] V)
  (hν : FunctionalInequalities.KLS.LogConcave ν) (hi : (interior (ν : MeasureTheory.Measure V).support).Nonempty)
Formal statement

theorem FunctionalInequalities.KLS.LogConcave.exists_mem_domain_closedWeightedOperator_toLp_mul_readable {φ : V → ℝ} (hφ : ContDiff ℝ ∞ φ)
    (hφc : HasCompactSupport φ) (x : (hν.closedWeightedOperator m B hi).domain) :
    ∃ hmem : (memLp_mul_of_contDiff_hasCompactSupport (ν := ν) hφ hφc
        (x : Lp ℝ 2 (ν : Measure V))).toLp _ ∈ (hν.closedWeightedOperator m B hi).domain,
      ((hν.closedWeightedOperator m B hi ⟨_, hmem⟩ : Lp V 2 (ν : Measure V)) : V → V)
        =ᵐ[(ν : Measure V)] fun y =>
          φ y • ((hν.closedWeightedOperator m B hi x : Lp V 2 (ν : Measure V)) : V → V) y
            + (x : Lp ℝ 2 (ν : Measure V)) y • apply B φ y := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/KLS/LogConcave/WeightedGradient/Multiplier.lean#L124-L230 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.KLS.LogConcave.WeightedGradient.Multiplier`, declaration `FunctionalInequalities.KLS.LogConcave.exists_mem_domain_closedWeightedOperator_toLp_mul`; the repository is private at upload time).

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