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Compactly Cutoff Hessian Entries Are Global L2L^2L2

Proved
FunctionalInequalities.Probability.Stein.memLp_cutoff_mul_hessianMatrix_entry_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readableletwin2026log-concave

This formalizes a technical weak-Hessian integrability statement associated with Theorem 2.5 of Letwin (arXiv:2607.24164v1).

Let u,χ:Rn→Ru,\chi:\mathbb R^n\to\mathbb Ru,χ:Rn→R, assume that ∇u\nabla u∇u is globally Lipschitz, and let i,j∈Fin ni,j\in\mathrm{Fin}\ ni,j∈Fin n. If χ\chiχ is continuous and compactly supported, then the function x↦χ(x) hessianMatrix⁡(u)(x)ijx\mapsto\chi(x)\,\operatorname{hessianMatrix}(u)(x)_{ij}x↦χ(x)hessianMatrix(u)(x)ij​ belongs to L2L^2L2 with respect to Lebesgue volume:

χ(x) hessianMatrix⁡(u)(x)ij∈L2(Rn,dx).\chi(x)\,\operatorname{hessianMatrix}(u)(x)_{ij}\in L^2(\mathbb R^n,dx).χ(x)hessianMatrix(u)(x)ij​∈L2(Rn,dx).

This provides a global L2L^2L2 cutoff interface for a Hessian entry that is controlled locally by the Lipschitz gradient assumption.

Original Lean declaration header

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

theorem memLp_cutoff_mul_hessianMatrix_entry {n : ℕ}
    {u χ : EuclideanSpace ℝ (Fin n) → ℝ} {G : NNReal}
    (hG : LipschitzWith G (gradient u)) (i j : Fin n)
    (hχ : Continuous χ) (hχc : HasCompactSupport χ) :
    MemLp (fun x => χ x * hessianMatrix u x i j) 2
      (volume : Measure (EuclideanSpace ℝ (Fin n)))

Module navigation

Module: Analysis/BakryEmery/WeakHessian.

Definition modules: Analysis/BakryEmery/HessianOperator.

Supporting theorems: norm_hessianMatrix_entry_le_of_lipschitz_gradient.

Previous: norm_hessianMatrix_entry_le_of_lipschitz_gradient · Next: chain_coordinate_of_differentiableAt_gradient.

Source excerpt, lines 451–456 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_BakryEmery_WeakHessian

open MeasureTheory Set
Formal statement

theorem FunctionalInequalities.Probability.Stein.memLp_cutoff_mul_hessianMatrix_entry_readable {n : ℕ}
    {u χ : EuclideanSpace ℝ (Fin n) → ℝ} {G : NNReal}
    (hG : LipschitzWith G (gradient u)) (i j : Fin n)
    (hχ : Continuous χ) (hχc : HasCompactSupport χ) :
    MemLp (fun x => χ x * hessianMatrix u x i j) 2
      (volume : Measure (EuclideanSpace ℝ (Fin n))) := by sorry
Source
Source: Letwin, The KLS constant is O(log^(1/4) n) (arXiv:2607.24164v1), Theorem 2.5. Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/BakryEmery/WeakHessian.lean#L449-L490 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.BakryEmery.WeakHessian`, declaration `FunctionalInequalities.Probability.Stein.memLp_cutoff_mul_hessianMatrix_entry`; the repository is private at upload time).

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