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Convergence Of Haar Pairings For Scalar Functions

Proved
FunctionalInequalities.KLS.LogConcave.tendsto_integral_mul_of_tendsto_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This theorem transfers scalar L2(ν)L^2(\nu)L2(ν) convergence to convergence of Haar integrals against compactly supported weights.

Let VVV be a finite-dimensional real vector space with the stated Borel and topological structures, let mmm be an additive Haar measure, let ν\nuν be a probability measure, and let U:V→RU:V\to\mathbb RU:V→R be continuous with ν=m.tilted⁡(−U)\nu=m.\operatorname{tilted}(-U)ν=m.tilted(−U). Suppose gn∈L2(ν)g_n\in L^2(\nu)gn​∈L2(ν) and the classes of gng_ngn​ converge to f∈L2(ν)f\in L^2(\nu)f∈L2(ν). For every continuous compactly supported ψ:V→R\psi:V\to\mathbb Rψ:V→R,

∫Vgn(x)ψ(x) dm(x)⟶∫Vf(x)ψ(x) dm(x).\int_V g_n(x)\psi(x)\,dm(x) \longrightarrow \int_V f(x)\psi(x)\,dm(x).∫V​gn​(x)ψ(x)dm(x)⟶∫V​f(x)ψ(x)dm(x).

This is a technical localization lemma used to pass L2(ν)L^2(\nu)L2(ν) limits through Haar integrals. No global integrability of the functions with respect to mmm is required beyond what follows from the tilted-law and compact-support hypotheses.

Original Lean declaration header

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

theorem tendsto_integral_mul_of_tendsto (hνU : (ν : Measure V) = m.tilted (-U))
    (hU : Continuous U) {g : ℕ → V → ℝ} (hg : ∀ n, MemLp (g n) 2 (ν : Measure V))
    {f : Lp ℝ 2 (ν : Measure V)} (hlim : Tendsto (fun n => (hg n).toLp (g n)) atTop (𝓝 f))
    {ψ : V → ℝ} (hψ : Continuous ψ) (hψc : HasCompactSupport ψ) :
    Tendsto (fun n => ∫ x, g n x * ψ x ∂m) atTop (𝓝 (∫ x, f x * ψ x ∂m))

Module navigation

Module: KLS/LogConcave/WeightedGradient/Kernel.

Supporting theorems: integrable_exp_neg_of_eq_tilted, integral_eq_integral_mul_exp.

Previous: restrict_le_smul_of_eq_tilted · Next: tendsto_integral_mul_inner_of_tendsto.

Source excerpt, lines 148–152 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_KLS_LogConcave_WeightedGradient_Kernel

open MeasureTheory Filter Topology InnerProductSpace
variable {V : Type*} [NormedAddCommGroup V] [InnerProductSpace ℝ V]
  [MeasurableSpace V] [BorelSpace V] [SecondCountableTopology V] [FiniteDimensional ℝ V]
  {ν : MeasureTheory.ProbabilityMeasure V} (m : MeasureTheory.Measure V) [m.IsAddHaarMeasure] {U : V → ℝ}
Formal statement

theorem FunctionalInequalities.KLS.LogConcave.tendsto_integral_mul_of_tendsto_readable (hνU : (ν : Measure V) = m.tilted (-U))
    (hU : Continuous U) {g : ℕ → V → ℝ} (hg : ∀ n, MemLp (g n) 2 (ν : Measure V))
    {f : Lp ℝ 2 (ν : Measure V)} (hlim : Tendsto (fun n => (hg n).toLp (g n)) atTop (𝓝 f))
    {ψ : V → ℝ} (hψ : Continuous ψ) (hψc : HasCompactSupport ψ) :
    Tendsto (fun n => ∫ x, g n x * ψ x ∂m) atTop (𝓝 (∫ x, f x * ψ x ∂m)) := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/KLS/LogConcave/WeightedGradient/Kernel.lean#L146-L230 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.KLS.LogConcave.WeightedGradient.Kernel`, declaration `FunctionalInequalities.KLS.LogConcave.tendsto_integral_mul_of_tendsto`; the repository is private at upload time).

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