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Centered Difference Bound for Moment-Map Potentials

Proved
FunctionalInequalities.Analysis.secondDifference_le_of_map_gradient_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readableletwin2026log-concave

This formalizes the centered-difference estimate associated with regularity of the moment map, cited in Lemma 2.2 of Letwin (arXiv:2607.24164v1) (Klartag [41]).

Let u,V:Rn→Ru,V:\mathbb R^n\to\mathbb Ru,V:Rn→R, let L∈[0,∞)L\in[0,\infty)L∈[0,∞), and assume:

  1. uuu is LLL-Lipschitz and convex;
  2. ∫e−u(x) dx=1\int e^{-u(x)}\,dx=1∫e−u(x)dx=1;
  3. K⊂RnK\subset\mathbb R^nK⊂Rn is compact and convex;
  4. VVV is C1C^1C1 and V(y)−κ2∥y∥2V(y)-\frac\kappa2\|y\|^2V(y)−2κ​∥y∥2 is convex for some κ>0\kappa>0κ>0;
  5. the pushforward of the tilted volume measure with density e−ue^{-u}e−u under ∇u\nabla u∇u equals the measure with density e−Ve^{-V}e−V on KKK;
  6. uuu is C1C^1C1.

Then, for every x,h∈Rnx,h\in\mathbb R^nx,h∈Rn,

uh2u(x):=u(x+h)+u(x−h)−2u(x)≤4κ∥h∥2.u_h^2u(x):=u(x+h)+u(x-h)-2u(x) \leq \frac{4}{\kappa}\|h\|^2.uh2​u(x):=u(x+h)+u(x−h)−2u(x)≤κ4​∥h∥2.

This exports the exact coefficient used in the subsequent Lipschitz-gradient regularity argument.

Original Lean declaration header

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

theorem secondDifference_le_of_map_gradient {n : ℕ} {u V : EuclideanSpace ℝ (Fin n) → ℝ}
    {L : NNReal} (hu : LipschitzWith L u) (hconv : ConvexOn ℝ univ u)
    (hnorm : ∫ x, Real.exp (-u x) ∂(volume : Measure (EuclideanSpace ℝ (Fin n))) = 1)
    {K : Set (EuclideanSpace ℝ (Fin n))} (hK : IsCompact K) (hKc : Convex ℝ K)
    (hV : ContDiff ℝ 1 V) {κ : ℝ} (hκ : 0 < κ)
    (hstrong : ConvexOn ℝ univ (fun y => V y - (κ / 2) * ‖y‖ ^ 2))
    (hmap : ((volume : Measure (EuclideanSpace ℝ (Fin n))).tilted (-u)).map (gradient u) =
      (volume : Measure (EuclideanSpace ℝ (Fin n))).withDensity
        (K.indicator fun p => ENNReal.ofReal (Real.exp (-V p))))
    (hC1 : ContDiff ℝ 1 u) :
    ∀ x h : EuclideanSpace ℝ (Fin n),
      u (x + h) + u (x - h) - 2 * u x ≤ (4 / κ) * ‖h‖ ^ 2

Module navigation

Module: Analysis/Convex/GradientLipschitz.

Selected supporting theorems: ae_exists_quadraticAt_expansion, exists_forall_le_and_isCompact_sublevel_of_convexOn_integrable_exp_neg, exists_penalized_maximum_with_comparison, posDef_and_convolution_le_log_det_hessianMatrix, posDef_and_det_eq_of_quadratic_expansion.

Source excerpt, lines 89–100 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Convex_GradientLipschitz

open MeasureTheory Set Metric Filter
Formal statement

theorem FunctionalInequalities.Analysis.secondDifference_le_of_map_gradient_readable {n : ℕ} {u V : EuclideanSpace ℝ (Fin n) → ℝ}
    {L : NNReal} (hu : LipschitzWith L u) (hconv : ConvexOn ℝ univ u)
    (hnorm : ∫ x, Real.exp (-u x) ∂(volume : Measure (EuclideanSpace ℝ (Fin n))) = 1)
    {K : Set (EuclideanSpace ℝ (Fin n))} (hK : IsCompact K) (hKc : Convex ℝ K)
    (hV : ContDiff ℝ 1 V) {κ : ℝ} (hκ : 0 < κ)
    (hstrong : ConvexOn ℝ univ (fun y => V y - (κ / 2) * ‖y‖ ^ 2))
    (hmap : ((volume : Measure (EuclideanSpace ℝ (Fin n))).tilted (-u)).map (gradient u) =
      (volume : Measure (EuclideanSpace ℝ (Fin n))).withDensity
        (K.indicator fun p => ENNReal.ofReal (Real.exp (-V p))))
    (hC1 : ContDiff ℝ 1 u) :
    ∀ x h : EuclideanSpace ℝ (Fin n),
      u (x + h) + u (x - h) - 2 * u x ≤ (4 / κ) * ‖h‖ ^ 2 := by sorry
Source
Source: Letwin, The KLS constant is O(log^(1/4) n) (arXiv:2607.24164v1), Lemma 2.2 (Klartag [41]). Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Convex/GradientLipschitz.lean#L88-L174 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Convex.GradientLipschitz`, declaration `FunctionalInequalities.Analysis.secondDifference_le_of_map_gradient`; the repository is private at upload time).

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