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Centered Classes Lie In The Diffusion Image Closure

Proved
FunctionalInequalities.KLS.LogConcave.HeatSemigroup.centered_mem_closure_compactSmoothDiffusionImage_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readableletwin2026log-concave

This formalizes the mean-zero density statement recorded as Lemma A.6 of Letwin (arXiv:2607.24164v1), with the reviewed relation recorded as partial.

Let VVV be finite-dimensional, let mmm be an additive Haar measure, let ν\nuν be log-concave with

ν=m.tilted⁡(−U),\nu=m.\operatorname{tilted}(-U),ν=m.tilted(−U),

where U∈C1(V)U\in C^1(V)U∈C1(V). If g∈L2(ν)g\in L^2(\nu)g∈L2(ν) has zero mean,

mean⁡ν(g)=0,\operatorname{mean}_\nu(g)=0,meanν​(g)=0,

then ggg belongs to the closure in L2(ν)L^2(\nu)L2(ν) of the literal images of smooth compactly supported functions under the diffusion operator compactSmoothDiffusionImage:

g∈{−Lf:f∈Cc∞(V)}‾.g\in\overline{\{ -Lf:f\in C_c^\infty(V)\}}.g∈{−Lf:f∈Cc∞​(V)}​.

The conclusion is a closure statement for every mean-zero L2L^2L2 class; it does not state the Brascamp–Lieb inequality itself.

Original Lean declaration header

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

theorem centered_mem_closure_compactSmoothDiffusionImage
    {ν : ProbabilityMeasure V} (m : Measure V) [m.IsAddHaarMeasure]
    (hν : FunctionalInequalities.KLS.LogConcave ν) {U : V → ℝ}
    (hνU : (ν : Measure V) = m.tilted (-U)) (hU : ContDiff ℝ 1 U)
    (g : Lp ℝ 2 (ν : Measure V)) (hg : L2.mean ν g = 0) :
    g ∈ closure (compactSmoothDiffusionImage hU)

Module navigation

Module: KLS/LogConcave/HeatSemigroup/Range.

Definition modules: L2/MeanVariance, Probability/LogConcave.

Supporting theorems: mem_closure_range_of_inner_eq_zero, eq_const_mean_of_closedWeightedOperator_eq_zero, exists_seq_smooth_tendsto_adjointComp_of_localRegularity, interior_support_nonempty_of_eq_tilted, toLp_mul_mem_closedWeightedOperator_domain_of_orthogonal.

Source excerpt, lines 45–50 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_KLS_LogConcave_HeatSemigroup_Range

open MeasureTheory Filter Topology Set InnerProductSpace
variable {V : Type*} [NormedAddCommGroup V] [InnerProductSpace ℝ V] [CompleteSpace V]
  [MeasurableSpace V] [BorelSpace V] [SecondCountableTopology V] [FiniteDimensional ℝ V]
Formal statement

theorem FunctionalInequalities.KLS.LogConcave.HeatSemigroup.centered_mem_closure_compactSmoothDiffusionImage_readable
    {ν : ProbabilityMeasure V} (m : Measure V) [m.IsAddHaarMeasure]
    (hν : FunctionalInequalities.KLS.LogConcave ν) {U : V → ℝ}
    (hνU : (ν : Measure V) = m.tilted (-U)) (hU : ContDiff ℝ 1 U)
    (g : Lp ℝ 2 (ν : Measure V)) (hg : L2.mean ν g = 0) :
    g ∈ closure (compactSmoothDiffusionImage hU) := by sorry
Source
Source: Letwin, The KLS constant is O(log^(1/4) n) (arXiv:2607.24164v1), Lemma A.6. Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/KLS/LogConcave/HeatSemigroup/Range.lean#L44-L115 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.KLS.LogConcave.HeatSemigroup.Range`, declaration `FunctionalInequalities.KLS.LogConcave.HeatSemigroup.centered_mem_closure_compactSmoothDiffusionImage`; the repository is private at upload time).

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