Scalar closed support gives zero product divergence integral
AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Divergence.integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_tsupport_subset_univ_pi_Ioo · theorem · Teaching coverage
Statement
Let a≤b, χ:P→ℝ, G:P→P and H=χG. Suppose H is continuous on K, has supplied derivative A(x) at each x∈O∖s for a countable s, and τ_A is integrable on K. Assume additionally tsupport χ⊆O. Then ∫_K δH dx=0.
All objects and hypotheses
- n∈ℕ, d=n+1≥1, P=(Fin d→ℝ) with its usual supremum norm, V=EuclideanSpace ℝ (Fin d) with its ℓ² norm. T:P→V is WithLp.toLp 2, a continuous linear equivalence, and e=T⁻¹=WithLp.ofLp.
- a,b∈P; K=[a,b]={x:∀i,a_i≤x_i≤b_i} is the closed box and O=∏_i(a_i,b_i) is the open box. All unspecified integrals and a.e. assertions use Lebesgue volume on P, restricted to K when indicated.
- a≤b coordinatewise. χ:P→ℝ and G:P→P are arbitrary factors; H(x)=χ(x)G(x). A:P→(P→L[ℝ]P) is a supplied derivative field for H.
- s⊆P is countable; H is continuous on K, and HasFDerivAt H (A x) x for every x∈O∖s.
- τ_A(x)=Σ_i(A(x)e_i)_i is integrable on K with respect to volume.
- tsupport χ⊆O.
Notation and interpretation
- Notation used below
For a raw field F:P→P, W_F=T∘F∘T⁻¹ and δF(x) is coordinateDivergence W_F at Tx. For a supplied linear-map field A, τ_A is its coordinate trace.
\[W_F:=T\circ F\circ T^{-1},\qquad\delta F(x):=\operatorname{Div}W_F(Tx),\qquad\tau_A(x):=\sum_i(A(x)e_i)_i.\]
Mathematical proof
1. Convert scalar closed support to ordinary support
Ordinary support is contained in its closure, so supp χ⊆O follows from tsupp χ⊆O.
Corresponding Lean step
support_subset_univ_pi_Ioo_of_tsupport_subset_univ_pi_Ioo hχtsupp.
2. Apply the existing scalar-support product theorem
Keep all product-level regularity and integrability assumptions unchanged and apply the scalar ordinary-support result.
Corresponding Lean step
integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_support_subset_univ_pi_Ioo ....
Lean statement · integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_tsupport_subset_univ_pi_Ioo
The hypotheses are imposed on the product χG directly. No separate continuity or differentiability of χ and G is assumed or derived; A need not be presented in product-rule form.
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 integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_tsupport_subset_univ_pi_Ioo
{n : ℕ}
(a b : Fin (n + 1) → ℝ) (hle : a ≤ b)
(χ : (Fin (n + 1) → ℝ) → ℝ)
(G : (Fin (n + 1) → ℝ) → Fin (n + 1) → ℝ)
(F' : (Fin (n + 1) → ℝ) →
(Fin (n + 1) → ℝ) →L[ℝ] (Fin (n + 1) → ℝ))
(s : Set (Fin (n + 1) → ℝ)) (hs : s.Countable)
(Hc : ContinuousOn (fun x => χ x • G x) (Set.Icc a b))
(Hd : ∀ x ∈ (Set.univ.pi fun i => Set.Ioo (a i) (b i)) \ s,
HasFDerivAt (fun x => χ x • G x) (F' x) x)
(Hi_trace : IntegrableOn (fun x => ∑ i, F' x (Pi.single i (1 : ℝ)) i)
(Set.Icc a b) volume)
(hχtsupp : tsupport χ ⊆ Set.univ.pi (fun i => Set.Ioo (a i) (b i))) :
∫ x in Set.Icc a b, coordinateDivergence
(fun y : EuclideanSpace ℝ (Fin (n + 1)) =>
(WithLp.toLp 2 ((χ (WithLp.ofLp y)) • G (WithLp.ofLp y)) :
EuclideanSpace ℝ (Fin (n + 1))))
(WithLp.toLp 2 x : EuclideanSpace ℝ (Fin (n + 1))) = 0Lean proof · integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_tsupport_subset_univ_pi_Ioo
The source first ordinary support is contained in its closure, so supp χ⊆O follows from tsupp χ⊆O. It finishes as follows: Keep all product-level regularity and integrability assumptions unchanged and apply the scalar ordinary-support result. Intermediate steps below identify the actual helper calls and the conditions each one needs.
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 integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_tsupport_subset_univ_pi_Ioo
{n : ℕ}
(a b : Fin (n + 1) → ℝ) (hle : a ≤ b)
(χ : (Fin (n + 1) → ℝ) → ℝ)
(G : (Fin (n + 1) → ℝ) → Fin (n + 1) → ℝ)
(F' : (Fin (n + 1) → ℝ) →
(Fin (n + 1) → ℝ) →L[ℝ] (Fin (n + 1) → ℝ))
(s : Set (Fin (n + 1) → ℝ)) (hs : s.Countable)
(Hc : ContinuousOn (fun x => χ x • G x) (Set.Icc a b))
(Hd : ∀ x ∈ (Set.univ.pi fun i => Set.Ioo (a i) (b i)) \ s,
HasFDerivAt (fun x => χ x • G x) (F' x) x)
(Hi_trace : IntegrableOn (fun x => ∑ i, F' x (Pi.single i (1 : ℝ)) i)
(Set.Icc a b) volume)
(hχtsupp : tsupport χ ⊆ Set.univ.pi (fun i => Set.Ioo (a i) (b i))) :
∫ x in Set.Icc a b, coordinateDivergence
(fun y : EuclideanSpace ℝ (Fin (n + 1)) =>
(WithLp.toLp 2 ((χ (WithLp.ofLp y)) • G (WithLp.ofLp y)) :
EuclideanSpace ℝ (Fin (n + 1))))
(WithLp.toLp 2 x : EuclideanSpace ℝ (Fin (n + 1))) = 0 :=
integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_support_subset_univ_pi_Ioo
a b hle χ G F' s hs Hc Hd Hi_trace
(support_subset_univ_pi_Ioo_of_tsupport_subset_univ_pi_Ioo hχtsupp)
/-- Finite-box coordinate-divergence integral vanishes for a cutoff-smul vector
field, deriving the continuity and off-countable Frechet differentiability
hypotheses from separate cutoff and vector-field regularity assumptions.
The trace integrability of the product-rule derivative remains an explicit
assumption. This theorem is still finite-box only: it does not prove smooth
cutoff construction, tail limits, whole-space weighted IBP, generator domains,
invariant laws, reversibility, or KL/FI. -/Scope and omitted-condition boundaries
- Only the finite box K is integrated. No passage to whole space, weighted integration by parts, generator domain, stationary law, or invariance follows from this wrapper alone.
- Derivative values at the boundary and on the countable exceptional set are not prescribed; equality of integrands is used a.e.
Source and reuse
ASTIS parents called
AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Divergence.support_subset_univ_pi_Ioo_of_tsupport_subset_univ_pi_IooAutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Divergence.integral_coordinateDivergence_toPi_box_eq_zero_of_scalar_support_subset_univ_pi_Ioo
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