Samplinglib
Lean gate not recorded for this source state main · 0e31a3cda412
ASTIS mathematical exposition

Almost-everywhere zero gradient gives zero shared information

AutoSamplingTheory.TechnicalLemmas.InformationTheory.RelativeFisher.information_eq_zero_of_gradient_ae_eq_zero · theorem · Teaching coverage

Statement

Let β be any measure on finite-dimensional Euclidean space E and q,r:E→ℝ arbitrary functions. If the totalized gradient of r is zero β-almost everywhere, then the shared information integral is zero. No positivity, normalization, or independent integrability premise is needed.

\[\nabla r=0\quad\beta\text{-a.e.}\quad\Longrightarrow\quad\mathcal I_\beta(q,r)=\int_Eq\|\nabla r\|^2\,d\beta=0.\]

All objects and hypotheses

  • ι is a finite index type (Fintype ι), and E=EuclideanSpace ℝ ι has its standard real inner product, topology and Borel measurable structure. Empty ι is allowed.
  • q and r are arbitrary functions E→ℝ. Their argument names do not assert that q is a density or r is a log-density; neither measurability nor differentiability is assumed.
  • β is an arbitrary measure on E, without probability or finiteness assumptions.
  • hgrad is the β-a.e. equality gradient r x=0. No differentiability or integrability condition on q or r is present.

Mathematical proof

1. Expose the defining integral

Unfold the shared information functional; the integrand remains the same weighted score energy.

\[\mathcal I_\beta(q,r)=\int_Ee_{q,r}\,d\beta.\]
Corresponding Lean step

RelativeFisher.information

2. Obtain a zero integrand on the supplied full-measure set

At every point where the supplied gradient vanishes, the preceding pointwise theorem gives zero energy regardless of the weight.

\[\nabla r(x)=0\Longrightarrow e_{q,r}(x)=0,\qquad e_{q,r}=0\quad\beta\text{-a.e.}.\]
Corresponding Lean step

filter_upwards; RelativeFisher.densityEnergy_eq_zero_of_gradient_eq_zero

3. Integrate the a.e. zero function

The Bochner integral of a function equal to zero a.e. is zero; no separate integrability hypothesis is needed for this theorem.

\[e_{q,r}=0\quad\beta\text{-a.e.}\Longrightarrow\int_E e_{q,r}\,d\beta=0.\]
Corresponding Lean step

MeasureTheory.integral_eq_zero_of_ae

Lean statement · information_eq_zero_of_gradient_ae_eq_zero

The a.e. premise refers to gradients themselves under the explicit base measure. It is not merely an assertion that r equals a constant a.e.

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 information_eq_zero_of_gradient_ae_eq_zero
    (base : Measure (State (ι := ι)))
    (q logRatio : State (ι := ι) → ℝ)
    (hgrad : ∀ᵐ x ∂base, gradient logRatio x = 0) :
    information base q logRatio = 0

Exact module and namespace context

Lean proof · information_eq_zero_of_gradient_ae_eq_zero

The proof first unfolds information, then transfers the supplied zero-gradient equality to a zero-energy equality a.e., and finally uses the zero-integral rule.

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 information_eq_zero_of_gradient_ae_eq_zero
    (base : Measure (State (ι := ι)))
    (q logRatio : State (ι := ι) → ℝ)
    (hgrad : ∀ᵐ x ∂base, gradient logRatio x = 0) :
    information base q logRatio = 0 := by
  rw [information]
  apply integral_eq_zero_of_ae
  filter_upwards [hgrad] with x hx
  exact densityEnergy_eq_zero_of_gradient_eq_zero hx

/-- The Fisher functional is insensitive to changing the supplied log-ratio on
an a.e. set *provided its gradients themselves agree a.e.*.  This is the exact
representative-level congruence needed after a Sobolev/Radon--Nikodym layer has
chosen versions. -/

Exact module and namespace context

Scope and omitted-condition boundaries

  • gradient is the totalized classical gradient, equal to zero at nondifferentiable points. No classical differentiability, weak derivative, Sobolev domain, or selected-representative/score equivalence follows merely from writing it.
  • An equality a.e. of function values does not by itself identify classical gradients. No pointwise choice on exceptional null sets or independence under arbitrary null-set changes is proved.
  • This is a sufficient condition, not a converse: zero totalized information does not in general imply a zero genuine score or equality of measures.

Source and reuse

ASTIS parents called

Mathlib API called (external library)

  • MeasureTheory.integral_eq_zero_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.