Samplinglib
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ASTIS mathematical exposition

Construct the algebraic carré du champ

AutoSamplingTheory.TechnicalLemmas.StochasticProcesses.CarreDuChamp.carreDuChamp · def · Teaching coverage

Statement

For any type X, any real-linear operator L on real functions on X, and f,g:X→ℝ, define ΓL(f,g) pointwise as one-half the defect between applying L to the product and the two product-rule terms. No measure or topology on X is needed.

\[\Gamma_L(f,g)(x):=\frac12\big(L(fg)(x)-f(x)Lg(x)-g(x)Lf(x)\big).\]

All objects and hypotheses

  • X is an arbitrary type, with no measurable-space, topological-space, normed-space, or finiteness hypothesis.
  • L:(X→ℝ)→ₗ[ℝ](X→ℝ) is a real-linear operator on all real-valued functions on X. No Markov semigroup or invariant law is supplied.
  • f,g:X→ℝ are arbitrary; products of functions are pointwise.

Construction and meaning

1. Compare operator-before-product and product-before-operator

Form L(fg), f Lg, and g Lf as functions on the same X.

\[A:=L(fg),\qquad B:=fLg,\qquad C:=gLf.\]
Corresponding Lean step

generator (f * g); generator g; generator f

2. Take the normalized defect

Subtract both product-rule terms and multiply by one-half, fixing the Γ convention used downstream.

\[\Gamma_L(f,g)=\tfrac12(A-B-C).\]
Corresponding Lean step

CarreDuChamp.carreDuChamp

Lean statement · carreDuChamp

L is a real-linear map, but not a proved Markov generator. The result is a function on X; no measurability or differential structure is assumed.

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 carreDuChamp
    (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
    (f g : X → ℝ) : X → ℝ

Exact module and namespace context

Lean construction · carreDuChamp

This definition performs the displayed pointwise algebra. It does not prove positivity or identify Γ with an inner product of gradients.

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 carreDuChamp
    (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
    (f g : X → ℝ) : X → ℝ :=
  fun x => (2 : ℝ)⁻¹ *
    (generator (f * g) x - f x * generator g x - g x * generator f x)

/-- The carre du champ is symmetric in its observable arguments. -/

Exact module and namespace context

Scope and omitted-condition boundaries

  • No global invariant-law, stationarity for every observable, reversibility, semigroup construction, or concrete diffusion identity is inferred from this declaration.
  • No Γ positivity, diffusion chain rule, or gradient formula follows from this definition alone.

Source and reuse

ASTIS parents called

    Mathlib API called (external library)

    • LinearMap

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.