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.
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.
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.
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.
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 = 0Lean 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. -/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
AutoSamplingTheory.TechnicalLemmas.InformationTheory.RelativeFisher.informationAutoSamplingTheory.TechnicalLemmas.InformationTheory.RelativeFisher.densityEnergy_eq_zero_of_gradient_eq_zero
Mathlib API called (external library)
- MeasureTheory.integral_eq_zero_of_ae
Mathematical sources
- Exact existing ASTIS declaration and body — Local source authority; renderer extracts the exact code.
- Bochner integral of an a.e. zero function — The exact final theorem has no separate integrability premise.
- Existing declaration-visibility check — Read-only #check evidence; no build/test run.
ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.