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Lipschitz Bound For Transported Diffusion Coefficients

Proved
FunctionalInequalities.Probability.Diffusion.diffusionCoeffs_lipschitz_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This auxiliary lemma concerns autonomous coefficients. Let b:(RN)→RNb:(\mathbb R^N)\to\mathbb R^Nb:(RN)→RN and σ:(RN)→(Rd→RN)\sigma:(\mathbb R^N)\to(\mathbb R^d\to\mathbb R^N)σ:(RN)→(Rd→RN), and let K∈[0,∞)K\in[0,\infty)K∈[0,∞) be an NNReal. If bbb is KKK-Lipschitz and every map x↦σ(x,k)x\mapsto\sigma(x,k)x↦σ(x,k) is KKK-Lipschitz, then there exists L∈RL\in\mathbb RL∈R such that the corresponding diffusionCoeffs b σ are IsLipschitz with respect to the zero measure on the unit space:

∃L,IsLipschitz⁡(diffusionCoeffs⁡(b,σ),0,L).\exists L,\quad \operatorname{IsLipschitz}(\operatorname{diffusionCoeffs}(b,\sigma),0,L).∃L,IsLipschitz(diffusionCoeffs(b,σ),0,L).

This declaration serves as a technical bridge from componentwise Lipschitz hypotheses to the bundled coefficient condition.

Original Lean declaration header

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

private theorem diffusionCoeffs_lipschitz {N d : ℕ}
    {b : (Fin N → ℝ) → Fin N → ℝ}
    {σ : (Fin N → ℝ) → Fin d → Fin N → ℝ} {K : ℝ≥0}
    (hb : LipschitzWith K b) (hσ : ∀ k, LipschitzWith K fun x => σ x k) :
    ∃ L : ℝ, (diffusionCoeffs b σ).IsLipschitz (0 : Measure Unit) L

Module navigation

Module: Probability/Diffusion/Existence.

Definition modules: LevyStochCalc/Ito/Setting.

Next: exists_isDiffusion.

Source excerpt, lines 109–113 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Probability_Diffusion_Existence

open MeasureTheory ProbabilityTheory Filter Topology
open scoped NNReal ENNReal
Formal statement
 theorem FunctionalInequalities.Probability.Diffusion.diffusionCoeffs_lipschitz_readable {N d : ℕ}
    {b : (Fin N → ℝ) → Fin N → ℝ}
    {σ : (Fin N → ℝ) → Fin d → Fin N → ℝ} {K : ℝ≥0}
    (hb : LipschitzWith K b) (hσ : ∀ k, LipschitzWith K fun x => σ x k) :
    ∃ L : ℝ, (FunctionalInequalities.Probability.Diffusion.diffusionCoeffs b σ).IsLipschitz (0 : Measure Unit) L := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Probability/Diffusion/Existence.lean#L109-L154 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Probability.Diffusion.Existence`, declaration `_private.FunctionalInequalities.Probability.Diffusion.Existence.0.FunctionalInequalities.Probability.Diffusion.diffusionCoeffs_lipschitz`; the repository is private at upload time).

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