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CarreDuChamp: mathematical reading route

Read the statements and derivations in source order. Every result has its own optional Lean statement and proof. Source assumptions, library reuse and unproved boundaries are kept explicit.

  1. Construct the algebraic carré du champ
  2. Symmetry of the algebraic carré du champ
  3. Construct the iterated carré du champ
  4. Symmetry of the iterated carré du champ
  5. The positive-parameter Bakry–Émery condition
  6. A Jensen gap yields nonnegative Γ through right-generator limits
  7. Pairwise integration by parts from three integrable terms
  8. Nonnegative Γ gives a nonnegative quadratic generator pairing
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.

    ASTIS mathematical exposition

    Symmetry of the algebraic carré du champ

    AutoSamplingTheory.TechnicalLemmas.StochasticProcesses.CarreDuChamp.carreDuChamp_comm · theorem · Teaching coverage

    Statement

    For any type X, real-linear L on real functions, and arbitrary f,g:X→ℝ, the functions ΓL(f,g) and ΓL(g,f) are equal everywhere. This symmetry is in the two observable arguments, not symmetry of L with respect to a measure.

    \[\Gamma_L(f,g)=\Gamma_L(g,f).\]

    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 and g are arbitrary real functions. No measure or stationarity premise.

    Mathematical proof

    1. Use commutativity of the pointwise product

    At every input y, f(y)g(y)=g(y)f(y), hence the input functions fg and gf are equal.

    \[fg=gf\quad\Longrightarrow\quad L(fg)=L(gf).\]
    Corresponding Lean step

    funext y; mul_comm

    2. Reorder the two subtraction terms

    At each x, the first operator term is unchanged and the two cross terms exchange places. Real arithmetic gives identical values.

    \[\tfrac12[L(fg)-fLg-gLf]=\tfrac12[L(gf)-gLf-fLg].\]
    Corresponding Lean step

    funext x; simp only [carreDuChamp, hfg]; ring

    Lean statement · carreDuChamp_comm

    The conclusion is equality of functions. It does not say ∫f Lg=∫g Lf; no measure even appears.

    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.

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

    Exact module and namespace context

    Lean proof · carreDuChamp_comm

    The proof first establishes equality of the pointwise product inputs, then unfolds Γ at an arbitrary state and rearranges the real terms.

    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.

    theorem carreDuChamp_comm
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f g : X → ℝ) :
        carreDuChamp generator f g = carreDuChamp generator g f := by
      funext x
      have hfg : f * g = g * f := by
        funext y
        exact mul_comm (f y) (g y)
      simp only [carreDuChamp, hfg]
      ring
    
    /-- Chewi Definition 1.2.28: the iterated carre du champ. -/

    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.
    • Argument symmetry of Γ is not reversibility or self-adjointness of L. No positivity claim is made.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • mul_comm

    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.

    ASTIS mathematical exposition

    Construct the iterated carré du champ

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

    Statement

    For any type X, real-linear L on real functions, and observables f,g, define Γ₂,L(f,g) as half of L applied to ΓL(f,g), minus the two mixed terms ΓL(f,Lg) and ΓL(g,Lf). This uses only the previously defined algebraic Γ.

    \[\Gamma_{2,L}(f,g):=\frac12\big[L(\Gamma_L(f,g))-\Gamma_L(f,Lg)-\Gamma_L(g,Lf)\big].\]

    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. Γ is the same normalized expression defined previously.

    Construction and meaning

    1. Build the three observable functions

    Apply L to Γ(f,g), and substitute Lg or Lf into the appropriate Γ argument.

    \[A:=L(\Gamma_L(f,g)),\quad B:=\Gamma_L(f,Lg),\quad C:=\Gamma_L(g,Lf).\]
    Corresponding Lean step

    CarreDuChamp.carreDuChamp; generator

    2. Take the normalized difference

    Combine those functions pointwise with the same factor one-half.

    \[\Gamma_{2,L}(f,g)=\tfrac12(A-B-C).\]
    Corresponding Lean step

    CarreDuChamp.iteratedCarreDuChamp

    Lean statement · iteratedCarreDuChamp

    The result is a real-valued function on X. The subscript two denotes this algebraic iterate; no Hessian or second-order differentiability assumption is encoded.

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

    Exact module and namespace context

    Lean construction · iteratedCarreDuChamp

    This construction applies L and Γ in the displayed arrangement and combines their real values. There is no curvature estimate to prove at this stage.

    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 iteratedCarreDuChamp
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f g : X → ℝ) : X → ℝ :=
      fun x => (2 : ℝ)⁻¹ *
        (generator (carreDuChamp generator f g) x
          - carreDuChamp generator f (generator g) x
          - carreDuChamp generator g (generator f) x)
    
    /-- The iterated carre du champ inherits symmetry from the first one. -/

    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 nonnegativity of Γ₂, curvature lower bound, Bochner identity, or concrete diffusion/Hessian identification is asserted.

    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.

    ASTIS mathematical exposition

    Symmetry of the iterated carré du champ

    AutoSamplingTheory.TechnicalLemmas.StochasticProcesses.CarreDuChamp.iteratedCarreDuChamp_comm · theorem · Teaching coverage

    Statement

    For any type X, real-linear L on real-valued functions, and arbitrary f,g, the functions Γ₂,L(f,g) and Γ₂,L(g,f) agree at every state.

    \[\Gamma_{2,L}(f,g)=\Gamma_{2,L}(g,f).\]

    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. No measure, topology, or curvature premise.

    Mathematical proof

    1. Keep the first term fixed using Γ symmetry

    The earlier theorem identifies Γ(f,g) and Γ(g,f), so their images under L agree.

    \[L(\Gamma_L(f,g))=L(\Gamma_L(g,f)).\]
    Corresponding Lean step

    CarreDuChamp.carreDuChamp_comm

    2. Swap the remaining terms

    After unfolding Γ₂, the other two terms appear in opposite order. Rearranging the subtraction proves the equality pointwise, hence as functions.

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

    funext x; simp only [iteratedCarreDuChamp, carreDuChamp_comm]; ring

    Lean statement · iteratedCarreDuChamp_comm

    This is symmetry in the two observable slots, not an integral or spectral symmetry statement.

    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.

    theorem iteratedCarreDuChamp_comm
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f g : X → ℝ) :
        iteratedCarreDuChamp generator f g =
          iteratedCarreDuChamp generator g f

    Exact module and namespace context

    Lean proof · iteratedCarreDuChamp_comm

    The proof unfolds the iterate, reuses symmetry of Γ for its operator input, and rearranges the two real cross terms.

    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.

    theorem iteratedCarreDuChamp_comm
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f g : X → ℝ) :
        iteratedCarreDuChamp generator f g =
          iteratedCarreDuChamp generator g f := by
      funext x
      simp only [iteratedCarreDuChamp, carreDuChamp_comm]
      ring
    
    /-- Chewi Definition 1.2.29: the curvature-dimension condition
    `CD(alpha, infinity)`, including the source requirement `alpha > 0`. -/

    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 curvature or integrated nonnegativity follows merely from this argument symmetry.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    No direct Mathlib call recorded; see the ASTIS parents.

    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.

    ASTIS mathematical exposition

    The positive-parameter Bakry–Émery condition

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

    Statement

    For a real-linear operator L on real functions on an arbitrary type X and a real α, SatisfiesBakryEmery L α means α>0 and Γ₂,L(f,f)(x)≥α ΓL(f,f)(x) for every real-valued function f and every state x. The formal predicate quantifies over all functions, with no hidden admissible-observable domain.

    \[\operatorname{BE}(L,\alpha)\ :\Longleftrightarrow\ \alpha>0\ \land\ \forall f:X\to\mathbb R\ \forall x\in X,\quad\alpha\Gamma_L(f,f)(x)\le\Gamma_{2,L}(f,f)(x).\]

    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.
    • α is any real input to the predicate; strict positivity is one of its required conjuncts.
    • No measure, regularity domain, probability law, or topology on X is present.

    Construction and meaning

    1. Require positive curvature parameter

    A certificate first provides the strict inequality, so this definition does not include zero or negative parameters.

    \[\alpha>0.\]
    Corresponding Lean step

    CarreDuChamp.SatisfiesBakryEmery: first conjunct

    2. Require the pointwise estimate for every observable and state

    A certificate must prove the Γ₂-versus-Γ comparison without an unstated restriction on f.

    \[\forall f,x,\qquad\Gamma_{2,L}(f,f)(x)-\alpha\Gamma_L(f,f)(x)\ge0.\]
    Corresponding Lean step

    CarreDuChamp.carreDuChamp; CarreDuChamp.iteratedCarreDuChamp

    Lean statement · SatisfiesBakryEmery

    This definition packages an inequality; it does not prove it. Its universal quantifiers are part of the exact domain, and no integral or a.e. qualifier occurs.

    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 SatisfiesBakryEmery
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (alpha : ℝ) : Prop

    Exact module and namespace context

    Lean construction · SatisfiesBakryEmery

    To establish the predicate for a model one must prove positive α and the displayed estimate for all f and x. The definition itself supplies no such proof.

    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 SatisfiesBakryEmery
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (alpha : ℝ) : Prop :=
      0 < alpha ∧ ∀ (f : X → ℝ) (x : X),
        alpha * carreDuChamp generator f f x ≤
          iteratedCarreDuChamp generator f f x
    
    /-- Chewi Lemma 1.2.13: the Markov-semigroup Jensen inequality implies
    nonnegativity of the carre du champ after taking the right-generator limit.
    
    The theorem is pointwise.  `hf` and `hf2` are the actual right difference-
    quotient limits for `f` and `f²`; `hcontinuous` is strong/right continuity of
    the orbit at the selected state. -/

    Exact module and namespace context

    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.
    • This does not prove a Bakry–Émery criterion for LSI/Poincaré, a lower Ricci/Hessian bound, or a nontrivial ergodic property. The zero-operator test illustrates that no such additional structure is encoded.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    No direct Mathlib call recorded; see the ASTIS parents.

    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.

    ASTIS mathematical exposition

    A Jensen gap yields nonnegative Γ through right-generator limits

    AutoSamplingTheory.TechnicalLemmas.StochasticProcesses.CarreDuChamp.carreDuChamp_nonneg_of_markov_jensen_rightGenerator · theorem · Teaching coverage

    Statement

    Let X be any type, L a real-linear operator on real functions, P_h a family of arbitrary operators indexed by h∈ℝ≥0, f:X→ℝ, and x∈X fixed. Assume (P_h f(x))²≤P_h(f²)(x) for every h>0; as h↓0 through positive nonnegative-real times, the difference quotients for f and f² converge respectively to Lf(x) and L(f²)(x); and P_h f(x)→f(x). Then ΓL(f,f)(x)≥0.

    \[\begin{gathered}(P_hf(x))^2\le P_h(f^2)(x)\quad(h>0),\\\frac{P_hf(x)-f(x)}h\to Lf(x),\qquad\frac{P_h(f^2)(x)-f(x)^2}h\to L(f^2)(x),\qquad P_hf(x)\to f(x)\\\Longrightarrow\quad0\le\Gamma_L(f,f)(x)=\tfrac12\big[L(f^2)(x)-2f(x)Lf(x)\big].\end{gathered}\]

    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.
    • P:ℝ≥0→(X→ℝ)→X→ℝ is an arbitrary family; no linearity, semigroup law, positivity-preserving property, or value at h=0 is assumed of P.
    • f:X→ℝ and x∈X are fixed. hjensen holds for every positive h∈ℝ≥0, for this f and this x.
    • hf is convergence of (P_hf(x)−f(x))/(h:ℝ) to Lf(x); hf2 is convergence of (P_h(f*f)(x)−f(x)²)/(h:ℝ) to L(f*f)(x). Both filters are nhdsWithin 0 (Ioi 0) on ℝ≥0.
    • hcontinuous asserts only P_hf(x)→f(x) along that same right-hand filter. It is not operator strong continuity or continuity at every state.

    Mathematical proof

    1. Define the nonnegative scaled Jensen gap

    For h>0 the numerator is nonnegative by hjensen and the denominator is positive, so the gap is nonnegative on the right-hand filter.

    \[G(h):=\frac{P_h(f^2)(x)-(P_hf(x))^2}{2h}\ge0\quad(h>0).\]
    Corresponding Lean step

    gap; hgap_nonneg; self_mem_nhdsWithin; div_nonneg

    2. Factor the difference of squares

    Subtract and add f(x)², then factor (P_hf(x))²−f(x)². This algebra is used only for h>0, where cancellation is valid.

    \[G(h)=\tfrac12\frac{P_h(f^2)(x)-f(x)^2}{h}-\tfrac12\frac{P_hf(x)-f(x)}h\big(P_hf(x)+f(x)\big).\]
    Corresponding Lean step

    hrewrite; field_simp; ring

    3. Take the sum and product limits

    The supplied right-continuity gives P_hf(x)+f(x)→2f(x). Combine this with hf to identify the product's limit.

    \[P_hf(x)+f(x)\to2f(x),\qquad\frac{P_hf(x)-f(x)}h\big(P_hf(x)+f(x)\big)\to2f(x)Lf(x).\]
    Corresponding Lean step

    tendsto_const_nhds; Filter.Tendsto.add; Filter.Tendsto.mul

    4. Identify the gap limit with Γ

    Use hf2 and the preceding product limit, multiply by one-half, subtract, and unfold the diagonal Γ formula. The eventually valid factorization transfers the limit back to G.

    \[G(h)\to\tfrac12L(f^2)(x)-\tfrac12Lf(x)(2f(x))=\Gamma_L(f,f)(x).\]
    Corresponding Lean step

    Filter.Tendsto.const_mul; Filter.Tendsto.sub; hvalue; Filter.Tendsto.congr'

    5. Pass nonnegativity through the limit

    The half-line [0,∞) is closed, and G is eventually in it along a nontrivial right-hand time filter. Therefore its limit lies there too.

    \[G(h)\ge0\ \text{eventually},\quad G(h)\to\Gamma_L(f,f)(x)\Longrightarrow\Gamma_L(f,f)(x)\ge0.\]
    Corresponding Lean step

    isClosed_Ici.mem_of_tendsto hlimit hgap_nonneg

    Lean statement · carreDuChamp_nonneg_of_markov_jensen_rightGenerator

    The three convergence assumptions are scalar assertions at one selected state. No topology or measurable structure on X is needed. The theorem name describes the motivating Markov route, but the formal inputs already supply its Jensen and generator-limit consequences.

    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.

    theorem carreDuChamp_nonneg_of_markov_jensen_rightGenerator
        (P : ℝ≥0 → (X → ℝ) → X → ℝ)
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f : X → ℝ) (x : X)
        (hjensen : ∀ h : ℝ≥0, 0 < h → (P h f x) ^ 2 ≤ P h (f * f) x)
        (hf : Tendsto
          (fun h : ℝ≥0 => (P h f x - f x) / (h : ℝ))
          (𝓝[>] 0) (𝓝 (generator f x)))
        (hf2 : Tendsto
          (fun h : ℝ≥0 => (P h (f * f) x - (f x) ^ 2) / (h : ℝ))
          (𝓝[>] 0) (𝓝 (generator (f * f) x)))
        (hcontinuous : Tendsto (fun h : ℝ≥0 => P h f x)
          (𝓝[>] 0) (𝓝 (f x))) :
        0 ≤ carreDuChamp generator f f x

    Exact module and namespace context

    Lean proof · carreDuChamp_nonneg_of_markov_jensen_rightGenerator

    The proof constructs a nonnegative gap, factors a difference of squares, applies the supplied three limits, and uses closedness of the nonnegative real half-line. It never constructs P, establishes a semigroup law, or derives the difference-quotient hypotheses.

    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.

    theorem carreDuChamp_nonneg_of_markov_jensen_rightGenerator
        (P : ℝ≥0 → (X → ℝ) → X → ℝ)
        (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f : X → ℝ) (x : X)
        (hjensen : ∀ h : ℝ≥0, 0 < h → (P h f x) ^ 2 ≤ P h (f * f) x)
        (hf : Tendsto
          (fun h : ℝ≥0 => (P h f x - f x) / (h : ℝ))
          (𝓝[>] 0) (𝓝 (generator f x)))
        (hf2 : Tendsto
          (fun h : ℝ≥0 => (P h (f * f) x - (f x) ^ 2) / (h : ℝ))
          (𝓝[>] 0) (𝓝 (generator (f * f) x)))
        (hcontinuous : Tendsto (fun h : ℝ≥0 => P h f x)
          (𝓝[>] 0) (𝓝 (f x))) :
        0 ≤ carreDuChamp generator f f x := by
      let gap : ℝ≥0 → ℝ := fun h =>
        (P h (f * f) x - (P h f x) ^ 2) / (2 * (h : ℝ))
      have hgap_nonneg : ∀ᶠ h in 𝓝[>] (0 : ℝ≥0), 0 ≤ gap h := by
        filter_upwards [self_mem_nhdsWithin] with h hh
        have hh0 : 0 < h := by simpa only [mem_Ioi] using hh
        exact div_nonneg (sub_nonneg.mpr (hjensen h hh0)) (by positivity)
      have hrewrite : ∀ᶠ h in 𝓝[>] (0 : ℝ≥0),
          gap h =
            (2 : ℝ)⁻¹ * ((P h (f * f) x - (f x) ^ 2) / (h : ℝ)) -
            (2 : ℝ)⁻¹ * (((P h f x - f x) / (h : ℝ)) *
              (P h f x + f x)) := by
        filter_upwards [self_mem_nhdsWithin] with h hh
        have hh0 : (h : ℝ) ≠ 0 := by
          have : 0 < h := by simpa only [mem_Ioi] using hh
          exact_mod_cast this.ne'
        dsimp [gap]
        field_simp
        ring
      have hlimit : Tendsto gap (𝓝[>] (0 : ℝ≥0))
          (𝓝 (carreDuChamp generator f f x)) := by
        have hconst : Tendsto (fun _ : ℝ≥0 => f x)
            (nhdsWithin 0 (Set.Ioi 0)) (nhds (f x)) :=
          tendsto_const_nhds
        have hsum : Tendsto (fun h : ℝ≥0 => P h f x + f x)
            (nhdsWithin 0 (Set.Ioi 0)) (nhds (f x + f x)) :=
          hcontinuous.add hconst
        have hprod := hf.mul hsum
        have hcombined := (hf2.const_mul (2 : ℝ)⁻¹).sub
          (hprod.const_mul (2 : ℝ)⁻¹)
        have hvalue :
            (2 : ℝ)⁻¹ * generator (f * f) x -
                (2 : ℝ)⁻¹ * (generator f x * (f x + f x)) =
              carreDuChamp generator f f x := by
          simp only [carreDuChamp]
          ring
        have htarget : Tendsto
            (fun h : ℝ≥0 =>
              (2 : ℝ)⁻¹ * ((P h (f * f) x - (f x) ^ 2) / (h : ℝ)) -
              (2 : ℝ)⁻¹ * (((P h f x - f x) / (h : ℝ)) *
                (P h f x + f x)))
            (𝓝[>] 0) (𝓝 (carreDuChamp generator f f x)) := by
          rw [← hvalue]
          exact hcombined
        exact htarget.congr' (hrewrite.mono fun h hh => hh.symm)
      exact isClosed_Ici.mem_of_tendsto hlimit hgap_nonneg
    
    /-- Chewi Theorem 1.2.14: stationarity and generator symmetry imply the
    fundamental integration-by-parts identity between the Dirichlet form and the
    integrated carre du champ.
    
    The three integrability hypotheses are the exact terms expanded from Gamma;
    they prevent the totalized Bochner integral from hiding a domain failure. -/

    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.
    • The conclusion is only at the supplied f and x; a global statement requires those inputs for every relevant observable and state.
    • The limit excludes h=0; no division-by-zero argument or two-sided derivative is used.
    • No diffusion chain rule or invariant-law theorem is produced.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • self_mem_nhdsWithin
    • div_nonneg
    • sub_nonneg
    • tendsto_const_nhds
    • Filter.Tendsto.add
    • Filter.Tendsto.mul
    • Filter.Tendsto.const_mul
    • Filter.Tendsto.sub
    • Filter.Tendsto.congr'
    • isClosed_Ici
    • IsClosed.mem_of_tendsto

    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.

    ASTIS mathematical exposition

    Pairwise integration by parts from three integrable terms

    AutoSamplingTheory.TechnicalLemmas.StochasticProcesses.CarreDuChamp.fundamental_integration_by_parts · theorem · Teaching coverage

    Statement

    Let X be a measurable space, μ any measure, L a real-linear operator on real functions, and f,g:X→ℝ. Assume L(fg), fLg and gLf are μ-integrable; ∫L(fg)dμ=0; and ∫fLg dμ=∫gLf dμ. Then the two Dirichlet pairings agree and their common value equals ∫ΓL(f,g)dμ. Stationarity and symmetry are required only for these displayed observables and integrals.

    \[\begin{gathered}L(fg),fLg,gLf\in L^1(\mu),\qquad\int_XL(fg)\,d\mu=0,\qquad\int_XfLg\,d\mu=\int_XgLf\,d\mu\\\Longrightarrow\quad\mathcal E_\mu^L(f,g)=\mathcal E_\mu^L(g,f),\qquad\mathcal E_\mu^L(f,g)=\int_X\Gamma_L(f,g)\,d\mu.\end{gathered}\]

    All objects and hypotheses

    • X is any measurable space; μ is any measure on X. No probability, finite measure, σ-finiteness, topology or norm on X is assumed.
    • 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 observables.
    • hLfg proves Integrable L(fg) μ; hfLg proves Integrable (fLg) μ; hgLf proves Integrable (gLf) μ. Each includes a.e. strong measurability and finite norm integral.
    • hstationary is the single equality ∫L(fg)dμ=0. hsymmetric is the single equality ∫fLg dμ=∫gLf dμ; neither is quantified over all observables.

    Mathematical proof

    1. Prepare integrability for the subtraction

    The first two assumptions imply integrability of L(fg)−fLg. The third integrable term gLf may then be subtracted from that difference.

    \[L(fg)-fLg\in L^1(\mu).\]
    Corresponding Lean step

    MeasureTheory.Integrable.sub; hsub

    2. Expand the Γ integral legitimately

    Pull out one-half and use the two subtraction rules, whose integrability premises are now available.

    \[\int_X\Gamma_L(f,g)\,d\mu=\tfrac12\left[\int_XL(fg)\,d\mu-\int_XfLg\,d\mu-\int_XgLf\,d\mu\right].\]
    Corresponding Lean step

    MeasureTheory.integral_const_mul; MeasureTheory.integral_sub; hgammaIntegral

    3. Prove equality of the two pairings

    The definition negates the two cross integrals; their assumed equality therefore gives symmetric pairings for this pair.

    \[\mathcal E_\mu^L(f,g)=-\int_XfLg\,d\mu=-\int_XgLf\,d\mu=\mathcal E_\mu^L(g,f).\]
    Corresponding Lean step

    Generator.dirichletForm; hsymmetric

    4. Use the supplied zero product integral and pairing symmetry

    Set A=∫fLg dμ. The expansion becomes one-half of 0−A−A, hence equals −A, which is the Dirichlet pairing.

    \[\int_X\Gamma_L(f,g)\,d\mu=\tfrac12(0-A-A)=-A=\mathcal E_\mu^L(f,g).\]
    Corresponding Lean step

    hstationary; hsymmetric; Generator.dirichletForm; ring

    5. Collect both conclusions

    The theorem returns the conjunction of pair symmetry and the integrated Γ identity, not a global property of L.

    \[[\mathcal E_\mu^L(f,g)=\mathcal E_\mu^L(g,f)]\ \land\ [\mathcal E_\mu^L(f,g)=\textstyle\int_X\Gamma_L(f,g)\,d\mu].\]
    Corresponding Lean step

    constructor

    Lean statement · fundamental_integration_by_parts

    There are three integrability proofs and two exact integral identities. They are explicit local hypotheses rather than an assumption that μ is a globally stationary reversible law. The conclusion contains two equalities joined by conjunction.

    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.

    theorem fundamental_integration_by_parts
        [MeasurableSpace X]
        (mu : Measure X) (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f g : X → ℝ)
        (hLfg : Integrable (generator (f * g)) mu)
        (hfLg : Integrable (fun x => f x * generator g x) mu)
        (hgLf : Integrable (fun x => g x * generator f x) mu)
        (hstationary : (∫ x, generator (f * g) x ∂mu) = 0)
        (hsymmetric :
          (∫ x, f x * generator g x ∂mu) =
            ∫ x, g x * generator f x ∂mu) :
        FunctionalInequalities.Generator.dirichletForm mu generator f g =
            FunctionalInequalities.Generator.dirichletForm mu generator g f ∧
          FunctionalInequalities.Generator.dirichletForm mu generator f g =
            ∫ x, carreDuChamp generator f g x ∂mu

    Exact module and namespace context

    Lean proof · fundamental_integration_by_parts

    The proof splits the Γ integral using the three integrability inputs, substitutes the local zero-integral and symmetry equations, and simplifies real arithmetic. The same symmetry equation directly proves the first equality.

    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.

    theorem fundamental_integration_by_parts
        [MeasurableSpace X]
        (mu : Measure X) (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f g : X → ℝ)
        (hLfg : Integrable (generator (f * g)) mu)
        (hfLg : Integrable (fun x => f x * generator g x) mu)
        (hgLf : Integrable (fun x => g x * generator f x) mu)
        (hstationary : (∫ x, generator (f * g) x ∂mu) = 0)
        (hsymmetric :
          (∫ x, f x * generator g x ∂mu) =
            ∫ x, g x * generator f x ∂mu) :
        FunctionalInequalities.Generator.dirichletForm mu generator f g =
            FunctionalInequalities.Generator.dirichletForm mu generator g f ∧
          FunctionalInequalities.Generator.dirichletForm mu generator f g =
            ∫ x, carreDuChamp generator f g x ∂mu := by
      have hsub : Integrable
          (fun x => generator (f * g) x - f x * generator g x) mu :=
        hLfg.sub hfLg
      have hgammaIntegral :
          (∫ x, carreDuChamp generator f g x ∂mu) =
            (2 : ℝ)⁻¹ *
              ((∫ x, generator (f * g) x ∂mu) -
                (∫ x, f x * generator g x ∂mu) -
                ∫ x, g x * generator f x ∂mu) := by
        change
          (∫ x, (2 : ℝ)⁻¹ *
            (generator (f * g) x - f x * generator g x -
              g x * generator f x) ∂mu) = _
        rw [integral_const_mul,
          integral_sub hsub hgLf, integral_sub hLfg hfLg]
      constructor
      · simp only [FunctionalInequalities.Generator.dirichletForm]
        rw [hsymmetric]
      · simp only [FunctionalInequalities.Generator.dirichletForm]
        rw [hgammaIntegral, hstationary, hsymmetric]
        ring
    
    /-- Chewi Corollary 1.2.15: the negative reversible generator has a
    nonnegative quadratic form once Gamma is pointwise nonnegative. -/

    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.
    • The explicit integrability inputs prevent invalid linear expansion of totalized nonintegrable integrals; they must not be omitted.
    • This is algebraic generator integration by parts, not a spatial divergence theorem with boundary terms or a concrete gradient identity.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • MeasureTheory.Integrable.sub
    • MeasureTheory.integral_const_mul
    • MeasureTheory.integral_sub

    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.

    ASTIS mathematical exposition

    Nonnegative Γ gives a nonnegative quadratic generator pairing

    AutoSamplingTheory.TechnicalLemmas.StochasticProcesses.CarreDuChamp.negativeGenerator_quadratic_nonneg · theorem · Teaching coverage

    Statement

    Let X be a measurable space, μ any measure, L a real-linear operator on real functions and f:X→ℝ. Assume L(f²) and fLf are μ-integrable, ∫L(f²)dμ=0, and ΓL(f,f)(x)≥0 for every x∈X. Then the negative-generator quadratic pairing Eμ^L(f,f) is nonnegative. No separate symmetry premise is required on the diagonal.

    \[\begin{gathered}L(f^2),fLf\in L^1(\mu),\qquad\int_XL(f^2)\,d\mu=0,\qquad\forall x,\ \Gamma_L(f,f)(x)\ge0\\\Longrightarrow\quad0\le\mathcal E_\mu^L(f,f)=-\int_X fLf\,d\mu.\end{gathered}\]

    All objects and hypotheses

    • X is any measurable space and μ any measure on X; no probability, finite-mass, σ-finiteness, or topological premise.
    • 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:X→ℝ is arbitrary. hLf2 proves Integrable L(f*f) μ and hfLf proves Integrable (fLf) μ.
    • hstationary is only ∫L(f*f)dμ=0. hgamma is pointwise nonnegativity ΓL(f,f)(x)≥0 for every x, not merely an a.e. assumption in the stated theorem.

    Mathematical proof

    1. Specialize the previous theorem to the repeated observable

    Use f in both slots. The two cross integrability obligations are the same supplied hfLf, and the symmetry equation becomes reflexive.

    \[\mathcal E_\mu^L(f,f)=\int_X\Gamma_L(f,f)\,d\mu.\]
    Corresponding Lean step

    CarreDuChamp.fundamental_integration_by_parts mu generator f f hLf2 hfLf hfLf hstationary rfl

    2. Pass the pointwise sign condition to an a.e. one

    A statement true at every x is true μ-almost everywhere, whatever μ is.

    \[\forall x,\ \Gamma_L(f,f)(x)\ge0\Longrightarrow\Gamma_L(f,f)\ge0\quad\mu\text{-a.e.}.\]
    Corresponding Lean step

    Filter.Eventually.of_forall hgamma

    3. Integrate nonnegativity

    The real integral of an a.e. nonnegative function is nonnegative. Combine this with the diagonal pairing identity.

    \[0\le\int_X\Gamma_L(f,f)\,d\mu=\mathcal E_\mu^L(f,f).\]
    Corresponding Lean step

    MeasureTheory.integral_nonneg_of_ae

    Lean statement · negativeGenerator_quadratic_nonneg

    The repeated observable removes the need for a separate pair-symmetry proof. Positivity of Γ is nevertheless a supplied pointwise hypothesis; the theorem does not derive it from a 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.

    theorem negativeGenerator_quadratic_nonneg
        [MeasurableSpace X]
        (mu : Measure X) (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f : X → ℝ)
        (hLf2 : Integrable (generator (f * f)) mu)
        (hfLf : Integrable (fun x => f x * generator f x) mu)
        (hstationary : (∫ x, generator (f * f) x ∂mu) = 0)
        (hgamma : ∀ x, 0 ≤ carreDuChamp generator f f x) :
        0 ≤ FunctionalInequalities.Generator.dirichletForm mu generator f f

    Exact module and namespace context

    Lean proof · negativeGenerator_quadratic_nonneg

    Apply the already-proved integration-by-parts identity on the diagonal, using the same pairing-integrability proof twice. Then integrate the supplied nonnegative Γ and rewrite the result as the quadratic pairing.

    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.

    theorem negativeGenerator_quadratic_nonneg
        [MeasurableSpace X]
        (mu : Measure X) (generator : (X → ℝ) →ₗ[ℝ] (X → ℝ))
        (f : X → ℝ)
        (hLf2 : Integrable (generator (f * f)) mu)
        (hfLf : Integrable (fun x => f x * generator f x) mu)
        (hstationary : (∫ x, generator (f * f) x ∂mu) = 0)
        (hgamma : ∀ x, 0 ≤ carreDuChamp generator f f x) :
        0 ≤ FunctionalInequalities.Generator.dirichletForm mu generator f f := by
      have hibp := fundamental_integration_by_parts mu generator f f
        hLf2 hfLf hfLf hstationary rfl
      rw [hibp.2]
      exact integral_nonneg_of_ae (Filter.Eventually.of_forall hgamma)
    
    end CarreDuChamp
    end StochasticProcesses
    end TechnicalLemmas
    end AutoSamplingTheory

    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 global reversibility or self-adjointness is proved. The conclusion concerns this one observable satisfying the listed local assumptions.
    • The Jensen-to-generator theorem is a possible separate producer of hgamma, but this proof does not call it or establish its limit hypotheses.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • Filter.Eventually.of_forall
    • MeasureTheory.integral_nonneg_of_ae

    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.