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ConditionalLocalizedResolvent: 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. Actual PBPS reflected resolvent: one solution before all cutoffs
Samplinglib mathematical lesson

Actual PBPS reflected resolvent: one solution before all cutoffs

AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalLocalizedResolvent.conditional_localized_resolvent · theorem · Teaching coverage

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Mathematical statement

From actual source V/alpha/beta/eta construct true common augmentation R/S and reflected law, and for each y the original dense closable D_y before epsilon/forcing. Construct one actual u before all cutoffs in that same closure domain. Retain the actual all-domain variational equation, ordinary locally integrable u/G, all C1 compact directional weak-gradient tests, squared local integrability of r=epsilon*u+inner(grad W_y,G)-f, compact-volume L2 of u/G/r and every C2 original distribution test. For every C2 compact chi define v=chi*u, Gchi=chi*G+u*grad chi, Fchi=chi*r+2 inner(grad chi,G)+u Delta chi. Prove genuine global-volume L2 of v/Gchi/Fchi and each topological support contained in tsupport chi. For every C1 compact psi/direction a both weak-gradient products are L1 with integral psi inner(Gchi,a)=-integral v Dpsi[a]. For every C2 compact phi both PDE products are L1 with integral v Delta phi=integral Fchi phi. The same u is chosen before all cutoffs; only smooth test/cutoff functions have classical derivatives. Reflected and unreflected RGO laws/operators/scales remain distinct.

\[S_y=(2x-y)_\#R_y=Z_y^{-1}e^{-W_y}dz,\ W_y(z)=V((y+z)/2)+\|z-y\|^2/(8\eta);\quad\exists D_y\ \forall\varepsilon>0,f\ \exists u\ \forall\chi\in C_c^2:\quad\nabla(\chi u)=\chi G+u\nabla\chi,\quad\Delta(\chi u)=\chi r+2\langle\nabla\chi,G\rangle+u\Delta\chi.\]
Lean statement · conditional_localized_resolvent

From actual source V/alpha/beta/eta construct true common augmentation R/S and reflected law, and for each y the original dense closable D_y before epsilon/forcing. Construct one actual u before all cutoffs in that same closure domain. Retain the actual all-domain variational equation, ordinary locally integrable u/G, all C1 compact directional weak-gradient tests, squared local integrability of r=epsilon*u+inner(grad W_y,G)-f, compact-volume L2 of u/G/r and every C2 original distribution test. For every C2 compact chi define v=chi*u, Gchi=chi*G+u*grad chi, Fchi=chi*r+2 inner(grad chi,G)+u Delta chi. Prove genuine global-volume L2 of v/Gchi/Fchi and each topological support contained in tsupport chi. For every C1 compact psi/direction a both weak-gradient products are L1 with integral psi inner(Gchi,a)=-integral v Dpsi[a]. For every C2 compact phi both PDE products are L1 with integral v Delta phi=integral Fchi phi. The same u is chosen before all cutoffs; only smooth test/cutoff functions have classical derivatives. Reflected and unreflected RGO laws/operators/scales remain distinct.

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 conditional_localized_resolvent {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : NNReal} {η : ℝ}
    (hα : 0 < (α:ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E, (α:ℝ)*‖v‖^2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β:ℝ)*‖v‖^2)
    (hη : 0 < η) (hβη : (β:ℝ)*η ≤ 1) :
    let μ := (volume : Measure E).tilted (fun x => -V x)
    let J := Measure.map (fun p : E × E => (p.1,p.1+Real.sqrt η • p.2)) (μ.prod (stdGaussian E))
    let W := fun y u : E => V ((1/2:ℝ) • (y+u)) + ‖u-y‖^2/(8*η)
    ∃ R S : Kernel E E, IsMarkovKernel R ∧ IsMarkovKernel S ∧
      (J.map Prod.swap).IsCondKernel R ∧
      (∀ y, S y = (R y).map (fun x => (2:ℝ) • x-y)) ∧
      ∀ y, S y = (volume : Measure E).tilted (fun u => -W y u) ∧
        ContDiff ℝ 2 (W y) ∧ Integrable (fun u => Real.exp (-W y u)) ∧
        0 < (∫ u, Real.exp (-W y u)) ∧
        ∃ D : Lp ℝ 2 (S y) →ₗ.[ℝ] Lp E 2 (S y),
          Dense (D.domain : Set (Lp ℝ 2 (S y))) ∧ D.IsClosable ∧ D.closure.IsClosed ∧
          (∀ (u : Lp ℝ 2 (S y)) (v : Lp E 2 (S y)), (u,v) ∈ D.graph ↔
            ∃ f : E → ℝ, ContDiff ℝ ∞ f ∧ HasCompactSupport f ∧
              u =ᵐ[S y] f ∧ v =ᵐ[S y] gradient f) ∧
      ∀ (ε : ℝ), 0 < ε → ∀ f : Lp ℝ 2 (S y),
      ∃ u : D.closure.domain,
        (∀ v : D.closure.domain,
          ε * ⟪(u : Lp ℝ 2 (S y)), (v : Lp ℝ 2 (S y))⟫ +
            ⟪D.closure u, D.closure v⟫ = ⟪f, (v : Lp ℝ 2 (S y))⟫) ∧
        LocallyIntegrable (fun x => (u : Lp ℝ 2 (S y)) x) ∧
        LocallyIntegrable (fun x => D.closure u x) ∧
        (∀ ψ : E → ℝ, ContDiff ℝ 1 ψ → HasCompactSupport ψ → ∀ v : E,
          Integrable (fun x => ψ x * inner ℝ (D.closure u x) v) ∧
          Integrable (fun x => (u : Lp ℝ 2 (S y)) x * fderiv ℝ ψ x v) ∧
          (∫ x, ψ x * inner ℝ (D.closure u x) v) =
            - ∫ x, (u : Lp ℝ 2 (S y)) x * fderiv ℝ ψ x v) ∧
        LocallyIntegrable (fun x => ‖ε * (u : Lp ℝ 2 (S y)) x +
          inner ℝ (gradient (W y) x) (D.closure u x) - f x‖ ^ 2) ∧
        (∀ K : Set E, IsCompact K →
          MemLp (fun x => (u : Lp ℝ 2 (S y)) x) 2 (volume.restrict K) ∧
          MemLp (fun x => D.closure u x) 2 (volume.restrict K) ∧
          MemLp (fun x => ε * (u : Lp ℝ 2 (S y)) x +
            inner ℝ (gradient (W y) x) (D.closure u x) - f x) 2 (volume.restrict K)) ∧
        (∀ φ : E → ℝ, ContDiff ℝ 2 φ → HasCompactSupport φ →
          Integrable (fun x => (u : Lp ℝ 2 (S y)) x * Laplacian.laplacian φ x) ∧
          Integrable (fun x => (ε * (u : Lp ℝ 2 (S y)) x +
            inner ℝ (gradient (W y) x) (D.closure u x) - f x) * φ x) ∧
          (∫ x, (u : Lp ℝ 2 (S y)) x * Laplacian.laplacian φ x) =
            ∫ x, (ε * (u : Lp ℝ 2 (S y)) x +
              inner ℝ (gradient (W y) x) (D.closure u x) - f x) * φ x) ∧
        ∀ χ : E → ℝ, ContDiff ℝ 2 χ → HasCompactSupport χ →
    let v := fun x => χ x * (u : Lp ℝ 2 (S y)) x
    let Gχ := fun x => χ x • D.closure u x + (u : Lp ℝ 2 (S y)) x • gradient χ x
    let Fχ := fun x => χ x * (ε * (u : Lp ℝ 2 (S y)) x + inner ℝ (gradient (W y) x) (D.closure u x) - f x) + 2 * inner ℝ (gradient χ x) (D.closure u x) +
      (u : Lp ℝ 2 (S y)) x * Laplacian.laplacian χ x
    MemLp v 2 volume ∧ MemLp Gχ 2 volume ∧ MemLp Fχ 2 volume ∧
    tsupport v ⊆ tsupport χ ∧ tsupport Gχ ⊆ tsupport χ ∧ tsupport Fχ ⊆ tsupport χ ∧
    (∀ ψ : E → ℝ, ContDiff ℝ 1 ψ → HasCompactSupport ψ → ∀ a : E,
      Integrable (fun x => ψ x * inner ℝ (Gχ x) a) ∧
      Integrable (fun x => v x * fderiv ℝ ψ x a) ∧
      (∫ x, ψ x * inner ℝ (Gχ x) a) = -∫ x, v x * fderiv ℝ ψ x a) ∧
    ∀ φ : E → ℝ, ContDiff ℝ 2 φ → HasCompactSupport φ →
      Integrable (fun x => v x * Laplacian.laplacian φ x) ∧
      Integrable (fun x => Fχ x * φ x) ∧
      (∫ x, v x * Laplacian.laplacian φ x) = ∫ x, Fχ x * φ x

Exact module and namespace context

Proof roadmap

  1. 1Construct the genuine common augmentation and reflection

    Apply the actual conditional gradient producer to source V/alpha/beta/eta. Retain common Markov R/S, disintegration of the actual Gaussian/Gibbs joint law, and selected reflected S_y for every y.

  2. 2Keep the exact reflected density and operator scale

    Retain true W_y(z)=V((y+z)/2)+norm(z-y)^2/(8eta), C2 regularity and the positive integrable partition. Reflected Hessian is quarter-scaled; the unreflected RGO law, Dirichlet form and epsilon parameter are not identified without an actual adapter.

  3. 3Fix D_y and then one u before all cutoffs

    For each fiber retain original dense closable exact compact-smooth D_y with closed closure before epsilon/forcing. The shared producer constructs u after those inputs, then all cutoffs use that u/G/r. No cutoff-specific operator or jointly measurable fiberwise solution is inserted.

  4. 4Derive every localized property from this actual fiber

    Rewrite only the actual S_y=volume.tilted(-W_y) equality and apply the shared same-D producer. Global volume L2/support and both tested weak equations are conclusions, not new paper assumptions; keep every parent variational/ordinary/local-L2 property.

  5. 5Exercise an actual nonquadratic law and unnormalized cutoff

    The focused Test uses V(x)=3x^2/8+sin(x)/8, genuine curvature in [1/2,1], eta in (0,1], an ordinary smooth bump chi with rIn1/rOut2 and the actual C2 test phi(x)=rho(x)(x^2)^(5/2), rho the normalized bump. For every source fiber/epsilon/forcing it extracts actual global L2/support, the particular directional weak-gradient equation at a=1, both PDE integrabilities and the localized PDE for the same u.

  6. 6Preserve the remaining full-paper and analytic obligations

    This proves fiberwise compact localization. It supplies no localized weighted-domain membership, joint measurable selector, H2/core/Bochner, Poincare/BL, noncompact domains, epsilon0, invariance/hypocoercivity/cost or either complete sampling/composition result. The original reflected/RGO contracts remain separate.

Full assumptions and notation

All objects and hypotheses

  • E has the same finite real Hilbert/Borel/full-space structure including dimension zero. Source V is C2 with all-point/all-direction alpha I≤Hess V≤beta I, alpha,beta NNReal, 0<alpha≤beta, eta>0 and beta eta≤1.
  • Actual common Gibbs/Gaussian augmentation J and Markov disintegrating R/S are constructed. S_y=(2x-y)#R_y is precisely volume.tilted(-W_y), W_y(z)=V((y+z)/2)+norm(z-y)^2/(8eta), with true C2 regularity and positive integrable partition.
  • For every y one original dense closable compact-smooth gradient D_y with closed closure is fixed before epsilon/input. For each epsilon>0 and true f in L2(S_y), one u/G/r serves all compact sets, ordinary tests and all C2 compact cutoffs. Existence is fiberwise; no jointly measurable solution selector, RGO operator identity or localized weighted-domain membership is concluded.

Mathematical proof

1. Construct the genuine common augmentation and reflection

Apply the actual conditional gradient producer to source V/alpha/beta/eta. Retain common Markov R/S, disintegration of the actual Gaussian/Gibbs joint law, and selected reflected S_y for every y.

\[J=(x,x+\sqrt\eta\xi)_\#(\mu\otimes\gamma),\quad S_y=(2x-y)_\#R_y.\]
Corresponding Lean step

ConditionalGradient.conditional_gradient_closable

2. Keep the exact reflected density and operator scale

Retain true W_y(z)=V((y+z)/2)+norm(z-y)^2/(8eta), C2 regularity and the positive integrable partition. Reflected Hessian is quarter-scaled; the unreflected RGO law, Dirichlet form and epsilon parameter are not identified without an actual adapter.

\[W_y(z)=V((y+z)/2)+\|z-y\|^2/(8\eta).\]
Corresponding Lean step

hSy,hW,hI,hZ; explicit Wy

3. Fix D_y and then one u before all cutoffs

For each fiber retain original dense closable exact compact-smooth D_y with closed closure before epsilon/forcing. The shared producer constructs u after those inputs, then all cutoffs use that u/G/r. No cutoff-specific operator or jointly measurable fiberwise solution is inserted.

\[\exists R,S\ \forall y\ \exists D_y\ \forall\varepsilon>0,f\ \exists u\ \forall\chi\in C_c^2.\]
Corresponding Lean step

hDense,hClose,hClosed,hgraph; ContDiff.of_le

4. Derive every localized property from this actual fiber

Rewrite only the actual S_y=volume.tilted(-W_y) equality and apply the shared same-D producer. Global volume L2/support and both tested weak equations are conclusions, not new paper assumptions; keep every parent variational/ordinary/local-L2 property.

\[v=\chi u,\quad G_\chi=\chi G+u\nabla\chi,\quad\Delta v=\chi r+2\langle\nabla\chi,G\rangle+u\Delta\chi\ \text{weakly}.\]
Corresponding Lean step

LocalizedWeakResolvent.localized_weak_resolvent

5. Exercise an actual nonquadratic law and unnormalized cutoff

The focused Test uses V(x)=3x^2/8+sin(x)/8, genuine curvature in [1/2,1], eta in (0,1], an ordinary smooth bump chi with rIn1/rOut2 and the actual C2 test phi(x)=rho(x)(x^2)^(5/2), rho the normalized bump. For every source fiber/epsilon/forcing it extracts actual global L2/support, the particular directional weak-gradient equation at a=1, both PDE integrabilities and the localized PDE for the same u.

\[\chi=\operatorname{bump}_{1,2},\quad\phi(x)=\rho(x)(x^2)^{5/2},\quad\int v\Delta\phi=\int F_\chi\phi.\]
Corresponding Lean step

Tests.LocalizedWeakResolvent.actual_nonconstant_cutoff_c2_pde

6. Preserve the remaining full-paper and analytic obligations

This proves fiberwise compact localization. It supplies no localized weighted-domain membership, joint measurable selector, H2/core/Bochner, Poincare/BL, noncompact domains, epsilon0, invariance/hypocoercivity/cost or either complete sampling/composition result. The original reflected/RGO contracts remain separate.

\[\text{same-solution cutoff edge};\quad H^2,\text{ core, BL and main results remain open}.\]
Corresponding Lean step

conditional_localized_resolvent

Lean verification

The exact proof begins folded immediately after the mathematical argument; the statement is folded beside the claim above.

Lean proof · conditional_localized_resolvent

Construct the genuine common augmentation and reflection; Keep the exact reflected density and operator scale; Fix D_y and then one u before all cutoffs; Derive every localized property from this actual fiber; Exercise an actual nonquadratic law and unnormalized cutoff; Preserve the remaining full-paper and analytic obligations

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.

Download module (.lean)
theorem conditional_localized_resolvent {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : NNReal} {η : ℝ}
    (hα : 0 < (α:ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E, (α:ℝ)*‖v‖^2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β:ℝ)*‖v‖^2)
    (hη : 0 < η) (hβη : (β:ℝ)*η ≤ 1) :
    let μ := (volume : Measure E).tilted (fun x => -V x)
    let J := Measure.map (fun p : E × E => (p.1,p.1+Real.sqrt η • p.2)) (μ.prod (stdGaussian E))
    let W := fun y u : E => V ((1/2:ℝ) • (y+u)) + ‖u-y‖^2/(8*η)
    ∃ R S : Kernel E E, IsMarkovKernel R ∧ IsMarkovKernel S ∧
      (J.map Prod.swap).IsCondKernel R ∧
      (∀ y, S y = (R y).map (fun x => (2:ℝ) • x-y)) ∧
      ∀ y, S y = (volume : Measure E).tilted (fun u => -W y u) ∧
        ContDiff ℝ 2 (W y) ∧ Integrable (fun u => Real.exp (-W y u)) ∧
        0 < (∫ u, Real.exp (-W y u)) ∧
        ∃ D : Lp ℝ 2 (S y) →ₗ.[ℝ] Lp E 2 (S y),
          Dense (D.domain : Set (Lp ℝ 2 (S y))) ∧ D.IsClosable ∧ D.closure.IsClosed ∧
          (∀ (u : Lp ℝ 2 (S y)) (v : Lp E 2 (S y)), (u,v) ∈ D.graph ↔
            ∃ f : E → ℝ, ContDiff ℝ ∞ f ∧ HasCompactSupport f ∧
              u =ᵐ[S y] f ∧ v =ᵐ[S y] gradient f) ∧
      ∀ (ε : ℝ), 0 < ε → ∀ f : Lp ℝ 2 (S y),
      ∃ u : D.closure.domain,
        (∀ v : D.closure.domain,
          ε * ⟪(u : Lp ℝ 2 (S y)), (v : Lp ℝ 2 (S y))⟫ +
            ⟪D.closure u, D.closure v⟫ = ⟪f, (v : Lp ℝ 2 (S y))⟫) ∧
        LocallyIntegrable (fun x => (u : Lp ℝ 2 (S y)) x) ∧
        LocallyIntegrable (fun x => D.closure u x) ∧
        (∀ ψ : E → ℝ, ContDiff ℝ 1 ψ → HasCompactSupport ψ → ∀ v : E,
          Integrable (fun x => ψ x * inner ℝ (D.closure u x) v) ∧
          Integrable (fun x => (u : Lp ℝ 2 (S y)) x * fderiv ℝ ψ x v) ∧
          (∫ x, ψ x * inner ℝ (D.closure u x) v) =
            - ∫ x, (u : Lp ℝ 2 (S y)) x * fderiv ℝ ψ x v) ∧
        LocallyIntegrable (fun x => ‖ε * (u : Lp ℝ 2 (S y)) x +
          inner ℝ (gradient (W y) x) (D.closure u x) - f x‖ ^ 2) ∧
        (∀ K : Set E, IsCompact K →
          MemLp (fun x => (u : Lp ℝ 2 (S y)) x) 2 (volume.restrict K) ∧
          MemLp (fun x => D.closure u x) 2 (volume.restrict K) ∧
          MemLp (fun x => ε * (u : Lp ℝ 2 (S y)) x +
            inner ℝ (gradient (W y) x) (D.closure u x) - f x) 2 (volume.restrict K)) ∧
        (∀ φ : E → ℝ, ContDiff ℝ 2 φ → HasCompactSupport φ →
          Integrable (fun x => (u : Lp ℝ 2 (S y)) x * Laplacian.laplacian φ x) ∧
          Integrable (fun x => (ε * (u : Lp ℝ 2 (S y)) x +
            inner ℝ (gradient (W y) x) (D.closure u x) - f x) * φ x) ∧
          (∫ x, (u : Lp ℝ 2 (S y)) x * Laplacian.laplacian φ x) =
            ∫ x, (ε * (u : Lp ℝ 2 (S y)) x +
              inner ℝ (gradient (W y) x) (D.closure u x) - f x) * φ x) ∧
        ∀ χ : E → ℝ, ContDiff ℝ 2 χ → HasCompactSupport χ →
    let v := fun x => χ x * (u : Lp ℝ 2 (S y)) x
    let Gχ := fun x => χ x • D.closure u x + (u : Lp ℝ 2 (S y)) x • gradient χ x
    let Fχ := fun x => χ x * (ε * (u : Lp ℝ 2 (S y)) x + inner ℝ (gradient (W y) x) (D.closure u x) - f x) + 2 * inner ℝ (gradient χ x) (D.closure u x) +
      (u : Lp ℝ 2 (S y)) x * Laplacian.laplacian χ x
    MemLp v 2 volume ∧ MemLp Gχ 2 volume ∧ MemLp Fχ 2 volume ∧
    tsupport v ⊆ tsupport χ ∧ tsupport Gχ ⊆ tsupport χ ∧ tsupport Fχ ⊆ tsupport χ ∧
    (∀ ψ : E → ℝ, ContDiff ℝ 1 ψ → HasCompactSupport ψ → ∀ a : E,
      Integrable (fun x => ψ x * inner ℝ (Gχ x) a) ∧
      Integrable (fun x => v x * fderiv ℝ ψ x a) ∧
      (∫ x, ψ x * inner ℝ (Gχ x) a) = -∫ x, v x * fderiv ℝ ψ x a) ∧
    ∀ φ : E → ℝ, ContDiff ℝ 2 φ → HasCompactSupport φ →
      Integrable (fun x => v x * Laplacian.laplacian φ x) ∧
      Integrable (fun x => Fχ x * φ x) ∧
      (∫ x, v x * Laplacian.laplacian φ x) = ∫ x, Fχ x * φ x := by
  obtain ⟨R,S,hR,hS,hcond,hSR,hfiber⟩ :=
    AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalGradient.conditional_gradient_closable hα hαβ hV hH hη hβη
  dsimp only
  refine ⟨R,S,hR,hS,hcond,hSR,?_⟩
  intro y
  obtain ⟨hSy,hW,hI,hZ,D,hDense,hClose,hClosed,hgraph⟩ := hfiber y
  refine ⟨hSy,hW,hI,hZ,D,hDense,hClose,hClosed,hgraph,?_⟩
  intro ε hε f
  let Wy : E → ℝ := fun u => V ((1/2:ℝ) • (y+u)) + ‖u-y‖^2/(8*η)
  have hW1 : ContDiff ℝ 1 Wy := hW.of_le (by norm_num)
  have hwc := AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.LocalizedWeakResolvent.localized_weak_resolvent Wy hW1 hI
  dsimp only at hwc
  rw [← hSy] at hwc
  exact hwc D hClose hgraph ε hε f

end AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalLocalizedResolvent

Exact module and namespace context

Scope and omitted-condition boundaries
  • Same actual original D/u/G/r, one u before all C2 compact chi: derive actual compact support/global volume L2 of chi*u, chi*G+u*gradchi, chi*r+2<gradchi,G>+u*Delta chi, all C1 compact weak-gradient and C2 compact distribution PDE with every L1 term legal. No localized weighted closure-domain membership, H2, Schwartz/tempered adapter, global core/Bochner, BL/Poincare, epsilon0, noncompact domain, measurable y-selector or either main/composition. Reflected/RGO law/scale remain distinct; TV never transfers unbounded expected cost.
Sources, dependencies, and reuse

Source and reuse

Samplinglib parents called

Mathlib API called (external library)

  • ContDiff.of_le

Mathematical sources

  • PBPS AppendixC.1 omitted analytic prerequisite — Same actual original D/u/G/r, one u before all C2 compact chi: derive actual compact support/global volume L2 of chi*u, chi*G+u*gradchi, chi*r+2<gradchi,G>+u*Delta chi, all C1 compact weak-gradient and C2 compact distribution PDE with every L1 term legal. No localized weighted closure-domain membership, H2, Schwartz/tempered adapter, global core/Bochner, BL/Poincare, epsilon0, noncompact domain, measurable y-selector or either main/composition. Reflected/RGO law/scale remain distinct; TV never transfers unbounded expected cost.

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