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Mixed Cumulant Entries of the Regular Suspension Joint Law

Proved
FunctionalInequalities.KLS.LogConcave.Suspension.source_mixed_cumulant_entries_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This auxiliary lemma states the mixed-cumulant entry identity corresponding to BKL equation (112).

Let n,N∈Nn,N\in\mathbb Nn,N∈N with N>0N>0N>0 and E=RnE=\mathbb R^nE=Rn (EuclideanSpace ℝ (Fin n)). Let hV:h_V:hV​: RegularPotential V a b′V\ a\ b'V a b′: a,b′>0a,b'>0a,b′>0, VVV is smooth with derivatives of polynomial growth, and aId⁡≤D2V≤b′Id⁡a\operatorname{Id}\le D^2V\le b'\operatorname{Id}aId≤D2V≤b′Id. Put μ=hV.law\mu=h_V.\text{law}μ=hV​.law, the normalized e−Ve^{-V}e−V law. Assume:

  1. β>0\beta>0β>0, f:E→Rf:E\to\mathbb Rf:E→R is C2C^2C2, and ∥D(Df)(x)∥≤M\|D(Df)(x)\|\le M∥D(Df)(x)∥≤M for every xxx.
  2. (β/N)M≤a(\beta/\sqrt N)M\le a(β/N​)M≤a.

Set σβ=1+2/β2\sigma_\beta=\sqrt{1+2/\beta^2}σβ​=1+2/β2​ (sigma) and λ=\lambda=λ= jointLaw μ f β σβ N\mu\ f\ \beta\ \sigma_\beta\ Nμ f β σβ​ N on H=H=H= JointSpace N nN\ nN n. Let e0,e(i,j)e_0,e_{(i,j)}e0​,e(i,j)​ be its scalar and copy-coordinate unit vectors, and (bj)(b_j)(bj​) the standard basis of EEE. For every natural d≥1d\ge1d≥1, I:Fin⁡d→Fin⁡NI:\operatorname{Fin}d\to\operatorname{Fin}NI:Find→FinN, and J:Fin⁡d→Fin⁡nJ:\operatorname{Fin}d\to\operatorname{Fin}nJ:Find→Finn,

κd+1λ(e0,e(Ik,Jk):k∈Fin⁡d)={d!σβN Tdμf(bJk:k∈Fin⁡d),∀k,k′, Ik=Ik′,0,otherwise.\kappa_{d+1}^{\lambda}(e_0,e_{(I_k,J_k)}:k\in\operatorname{Fin}d)= \begin{cases} \dfrac{d!}{\sigma_\beta\sqrt N}\,T_d^\mu f(b_{J_k}:k\in\operatorname{Fin}d),&\forall k,k',\ I_k=I_{k'},\\ 0,&\text{otherwise}. \end{cases}κd+1λ​(e0​,e(Ik​,Jk​)​:k∈Find)=⎩⎨⎧​σβ​N​d!​Tdμ​f(bJk​​:k∈Find),0,​∀k,k′, Ik​=Ik′​,otherwise.​

Here κd+1λ\kappa_{d+1}^{\lambda}κd+1λ​ is cumulant and TdμfT_d^\mu fTdμ​f is taylorTensor. The identity retains repeated spatial slots and supplies the entry data for cumulant norm estimates. Neither isotropy of μ\muμ nor centering or unit variance of fff is assumed.

Original Lean declaration header

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

private theorem source_mixed_cumulant_entries {n N : ℕ} (hN : 0 < N)
    (V : EuclideanSpace ℝ (Fin n) → ℝ) {a b' : ℝ} (hV : KLS.RegularPotential V a b')
    {f : EuclideanSpace ℝ (Fin n) → ℝ} {M β : ℝ} (hβ : 0 < β)
    (hf : ContDiff ℝ 2 f) (hfM : ∀ x, ‖fderiv ℝ (fderiv ℝ f) x‖ ≤ M)
    (h110 : β / Real.sqrt N * M ≤ a)
    {d : ℕ} (hd : 1 ≤ d) (I : Fin d → Fin N) (J : Fin d → Fin n) :
    cumulant (jointLaw (hV.law : Measure _) f β (sigma β) N) (d + 1)
      (Fin.cons (EuclideanSpace.single none 1)
        (fun k => EuclideanSpace.single (some (I k, J k)) 1)) =
      if ∀ k k', I k = I k' then
        (d.factorial : ℝ) / (sigma β * Real.sqrt N) *
          taylorTensor (hV.law : Measure _) f d
            (fun k => EuclideanSpace.basisFun (Fin n) ℝ (J k)) else 0

Module navigation

Module: KLS/BKL/Suspension/Derivatives.

Definition modules: KLS/BKL/Suspension/Law, Analysis/BakryEmery/Tilt/Basic, KLS/LogConcave/Regular/Basic, Analysis/BakryEmery/Cumulant/Basic.

Supporting theorems: contDiffOn_logLaplace_ball, exists_integrable_exp_norm, fderiv_logLaplace_jointLaw_none, jointLaw_eq_tilted, logConcave_jointLaw_of_eq_tilted.

Previous: fderiv_logLaplace_jointLaw_none.

Source excerpt, lines 713–725 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_KLS_BKL_Suspension_Derivatives

open MeasureTheory FunctionalInequalities FunctionalInequalities.Analysis.BakryEmery
set_option autoImplicit false
Formal statement

 theorem FunctionalInequalities.KLS.LogConcave.Suspension.source_mixed_cumulant_entries_readable {n N : ℕ} (hN : 0 < N)
    (V : EuclideanSpace ℝ (Fin n) → ℝ) {a b' : ℝ} (hV : KLS.RegularPotential V a b')
    {f : EuclideanSpace ℝ (Fin n) → ℝ} {M β : ℝ} (hβ : 0 < β)
    (hf : ContDiff ℝ 2 f) (hfM : ∀ x, ‖fderiv ℝ (fderiv ℝ f) x‖ ≤ M)
    (h110 : β / Real.sqrt N * M ≤ a)
    {d : ℕ} (hd : 1 ≤ d) (I : Fin d → Fin N) (J : Fin d → Fin n) :
    cumulant (jointLaw (hV.law : Measure _) f β (sigma β) N) (d + 1)
      (Fin.cons (EuclideanSpace.single none 1)
        (fun k => EuclideanSpace.single (some (I k, J k)) 1)) =
      if ∀ k k', I k = I k' then
        (d.factorial : ℝ) / (sigma β * Real.sqrt N) *
          taylorTensor (hV.law : Measure _) f d
            (fun k => EuclideanSpace.basisFun (Fin n) ℝ (J k)) else 0 := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/KLS/BKL/Suspension/Derivatives.lean#L711-L790 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.KLS.BKL.Suspension.Derivatives`, declaration `_private.FunctionalInequalities.KLS.BKL.Suspension.Derivatives.0.FunctionalInequalities.KLS.LogConcave.Suspension.source_mixed_cumulant_entries`; the repository is private at upload time).

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