production module
AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalBochner
Read the mathematical statements and proofs in order
1 named declarations scanned from AutoSamplingTheory/ExampleCases/ProximalBPS/ConditionalBochner.lean.
Declarations
theorem AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalBochner.conditional_bochner_energy Partial Not mapped
Read the complete mathematical statement and proof, with Lean below each
- Actual conditional normalized Bochner identity and curvature-energy bound. The displayed Hessian square term uses genuine directional derivatives in an orthonormal basis, not an assumed operator or a spectral-gap premise.
theorem conditional_bochner_energy {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)) ∧
∀ f : E → ℝ, ContDiff ℝ ∞ f → HasCompactSupport f →
let L := fun u => Laplacian.laplacian f u - inner ℝ (gradient (W y) u) (gradient f u)
let H := fun u => ∑ i, ‖gradient (fun z => fderiv ℝ f z ((stdOrthonormalBasis ℝ E) i)) u‖^2
let C := fun u => fderiv ℝ (fderiv ℝ (W y)) u (gradient f u) (gradient f u)
Integrable (fun u => (L u)^2) (S y) ∧
Integrable (fun u => ‖gradient f u‖^2) (S y) ∧
Integrable H (S y) ∧ Integrable C (S y) ∧
(∫ u, (L u)^2 ∂S y) = (∫ u, H u ∂S y) + ∫ u, C u ∂S y ∧
(((α:ℝ)+1/η)/4) * (∫ u, ‖gradient f u‖^2 ∂S y) ≤ ∫ u, (L u)^2 ∂S y := by
obtain ⟨R,S,hR,hS,hcond,hSR,hfiber⟩ :=
ConditionalScoreDomain.conditional_curvature_and_score_domain hα hαβ hV hH hη hβη
let _ : IsMarkovKernel S := hS
dsimp only
refine ⟨R,S,hR,hS,hcond,hSR,?_⟩
intro y
let W := fun u : E => V ((1/2:ℝ) • (y+u)) + ‖u-y‖^2/(8*η)
obtain ⟨hSy,hWC,_,_,hcurv,_,_⟩ := hfiber y
change S y = (volume : Measure E).tilted (fun u => -W u) at hSy
have hweight : Integrable (fun u => Real.exp (-W u)) := by
by_contra hn
have hz : S y = 0 := hSy.trans (tilted_of_not_integrable hn)
have hu := measure_univ (μ := S y)
rw [hz] at hu
norm_num at hu
have hZ : 0 < ∫ u, Real.exp (-W u) := integral_exp_pos hweight
refine ⟨hSy,hWC,hweight,hZ,?_⟩
intro f hf hc
obtain ⟨hL,hG,hHess,hC,hidentity,hbound⟩ :=
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