The generator Poincaré predicate
AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.Generator.SatisfiesPoincare · def · Teaching coverage
Statement
Given a measure μ on a measurable space E, any operator L on real functions, and C∈ℝ, SatisfiesPoincare means that μ is a probability measure, C>0, and every Poincare-admissible f satisfies Varμ(f)≤C Eμ^L(f,f). Admissibility requires μ-integrability of f, its centered square, and fLf.
All objects and hypotheses
- E is any measurable space and μ is any measure on E, unless probability normalization is explicitly included below. No topology, norm, finite dimension, σ-finiteness, or nonempty-space premise is imposed.
- L:(E→ℝ)→(E→ℝ) is an arbitrary operator on real functions. This signature does not require linearity, measurability preservation, Markovness, or a semigroup.
- C is an arbitrary real input to the predicate; positivity and probability normalization are required conjuncts, not ambient typeclass assumptions.
- The universal quantifier is over all real functions; the inequality is required only when all three PoincareAdmissible integrability conditions hold.
Construction and meaning
1. Require a probability measure and positive constant
These are the first two parts of a certificate for this predicate.
Corresponding Lean step
MeasureTheory.IsProbabilityMeasure; Generator.SatisfiesPoincare
2. Require the inequality on the explicit domain
For every f, a proof of the three admissibility conditions must yield the variance-versus-energy estimate.
Corresponding Lean step
Generator.PoincareAdmissible; Generator.variance; Generator.dirichletForm
Lean statement · SatisfiesPoincare
The probability condition is stored inside the proposition rather than assumed in brackets. The declaration does not require L to be a linear or Markov operator.
Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.
def SatisfiesPoincare
(mu : Measure E) (generator : (E → ℝ) → E → ℝ)
(C : ℝ) : PropLean construction · SatisfiesPoincare
This is a definition of what a Poincaré certificate must establish: normalization, positive C, and an estimate for every admissible observable. It does not produce such a certificate or prove decay.
Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.
def SatisfiesPoincare
(mu : Measure E) (generator : (E → ℝ) → E → ℝ)
(C : ℝ) : Prop :=
IsProbabilityMeasure mu ∧ 0 < C ∧
∀ f : E → ℝ, PoincareAdmissible mu generator f →
variance mu f ≤ C * dirichletForm mu generator f f
/-- Relative entropy of a density `rho` with respect to its reference
probability measure. Mathlib's totalized `Real.log 0 = 0` gives the standard
zero-density convention in the product `rho * log rho`. -/Scope and omitted-condition boundaries
- This defines a proposition that must be proved for a concrete model. It does not prove existence of a witness or derive an inequality from the operator's name.
- No global invariant-law, stationarity for every observable, reversibility, semigroup construction, or concrete diffusion identity is inferred from this declaration.
- No spectral-gap equivalence, semigroup variance dissipation, or exponential-convergence theorem is supplied.
Source and reuse
ASTIS parents called
AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.Generator.PoincareAdmissibleAutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.Generator.varianceAutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.Generator.dirichletForm
Mathlib API called (external library)
- MeasureTheory.IsProbabilityMeasure
Mathematical sources
- Exact existing ASTIS declaration — Current statement and body; renderer extracts exact Lean rather than duplicating it in this packet.
- Existing source-correspondence record: chewi-1-2-definition-1-2-19 — Existing local record for Chewi Definition1.2.19, edition2026-08-09, printed p.16/PDF p.28. Reported only within the current declaration's explicit assumptions; no new source audit or source-equivalence verdict.
- Probability-measure class — The class means μ(univ)=1; no extra state-space topology is introduced.
- Existing focused test — Declaration-visibility #check only. No test/build run for this packet.
ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.