production module
AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalGradient
Read the mathematical statements and proofs in order
1 named declarations scanned from AutoSamplingTheory/ExampleCases/ProximalBPS/ConditionalGradient.lean.
Declarations
theorem AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalGradient.conditional_gradient_closable Partial Not mapped
Read the complete mathematical statement and proof, with Lean below each
- The actual conditional Gibbs gradient has a dense smooth compact domain and a single-valued closed graph extension. The displayed graph equivalence fixes the genuine gradient, not an arbitrary abstract closable operator.
theorem conditional_gradient_closable {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 := by
obtain ⟨R,S,hR,hS,hcond,hSR,hfiber⟩ :=
ConditionalBochner.conditional_bochner_energy hα hαβ hV hH hη hβη
dsimp only
refine ⟨R,S,hR,hS,hcond,hSR,?_⟩
intro y
obtain ⟨hSy,hW,hI,hZ,_⟩ := hfiber y
refine ⟨hSy,hW,hI,hZ,?_⟩
rw [hSy]
exact WeightedGradient.compact_gradient_closable _ (hW.of_le (by norm_num)) hI
end AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalGradient
AutoSamplingTheory/ExampleCases/ProximalBPS/ConditionalGradient.lean:26published source at 0e31a3cda412