Haar Integrability Of Exponential Norm Decay
ProvedFunctionalInequalities.Probability.integrable_exp_sub_norm_readablefunctional-inequalitieskls-conjecturekls-modularkls-readablelog-concave
This is a finite-dimensional Haar-integrability estimate for exponential norm decay.
Let be finite-dimensional with a real inner product and Borel measurable structure, let be an additive Haar measure on , and let satisfy . For every ,
is integrable with respect to .
This supplies the integrable exponential envelope used in conjugate-mass estimates, including the zero-dimensional case.
Original Lean declaration header
Within namespace FunctionalInequalities.Probability. Shown in its original module context. Proof omitted.
theorem integrable_exp_sub_norm [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
(dx : Measure E) [dx.IsAddHaarMeasure] {c : ℝ} (hc : 0 < c) (M : ℝ) :
Integrable (fun y : E => Real.exp (M - c * ‖y‖)) dx
Module navigation
Module: Probability/MomentMeasure/ConjugateMass.
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Source excerpt, lines 36–38 · Frozen source 408915f.
Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Probability_MomentMeasure_ConjugateMass
open MeasureTheory Filter Set
variable {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
Formal statement
theorem FunctionalInequalities.Probability.integrable_exp_sub_norm_readable [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
(dx : Measure E) [dx.IsAddHaarMeasure] {c : ℝ} (hc : 0 < c) (M : ℝ) :
Integrable (fun y : E => Real.exp (M - c * ‖y‖)) dx := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Probability/MomentMeasure/ConjugateMass.lean#L34-L60 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Probability.MomentMeasure.ConjugateMass`, declaration `FunctionalInequalities.Probability.integrable_exp_sub_norm`; the repository is private at upload time).