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Strict Covariance Positivity from Full Affine Support

Proved
FunctionalInequalities.Covariance.inner_operator_self_pos_of_affineSupport_eq_top_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This proves strict positivity of the covariance operator under full affine support.

Let EEE be a finite-dimensional real inner-product space with its Borel measurable structure, and let μ\muμ be a probability measure on EEE such that id⁡∈L2(μ)\operatorname{id}\in L^2(\mu)id∈L2(μ). Assume that the affine support of μ\muμ is all of EEE:

affineSupport⁡(μ)=E.\operatorname{affineSupport}(\mu)=E.affineSupport(μ)=E.

Then every nonzero vector v∈Ev\in Ev∈E satisfies

0<⟨Cov⁡(μ)v,v⟩.0<\langle \operatorname{Cov}(\mu)v,v\rangle.0<⟨Cov(μ)v,v⟩.

Thus the ambient covariance quadratic form is positive definite under the stated support condition.

The result is useful for obtaining invertibility or coercivity of covariance on a full affine support.

Original Lean declaration header

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

theorem Covariance.inner_operator_self_pos_of_affineSupport_eq_top (μ : ProbabilityMeasure E)
    (h2 : MemLp id 2 (μ : Measure E)) (hfull : Probability.affineSupport μ = ⊤)
    {v : E} (hv : v ≠ 0) : 0 < ⟪Covariance.operator (μ : Measure E) v, v⟫

Module navigation

Module: Probability/Covariance/Positivity.

Definition modules: Probability/Covariance/Basic, Probability/AffineSupport.

Source excerpt, lines 21–23 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Probability_Covariance_Positivity

open MeasureTheory ProbabilityTheory
open scoped RealInnerProductSpace
variable {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
  [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
Formal statement

theorem FunctionalInequalities.Covariance.inner_operator_self_pos_of_affineSupport_eq_top_readable (μ : ProbabilityMeasure E)
    (h2 : MemLp id 2 (μ : Measure E)) (hfull : Probability.affineSupport μ = ⊤)
    {v : E} (hv : v ≠ 0) : 0 < ⟪Covariance.operator (μ : Measure E) v, v⟫ := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Probability/Covariance/Positivity.lean#L20-L46 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Probability.Covariance.Positivity`, declaration `FunctionalInequalities.Covariance.inner_operator_self_pos_of_affineSupport_eq_top`; the repository is private at upload time).

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