production module
AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalScoreDomain
Read the mathematical statements and proofs in order
1 named declarations scanned from AutoSamplingTheory/ExampleCases/ProximalBPS/ConditionalScoreDomain.lean.
Declarations
theorem AutoSamplingTheory.ExampleCases.ProximalBPS.ConditionalScoreDomain.conditional_curvature_and_score_domain Partial Not mapped
Read the complete mathematical statement and proof, with Lean below each
- The actual reflected conditional law has the source curvature and sharp score derivative bound, with directional scores in L2 and the stated finite variance/gradient-energy domain. No Poincare inequality is assumed or proved.
theorem conditional_curvature_and_score_domain {E : Type u} [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)
let s := fun y u : E => -(1/2:ℝ) • fderiv ℝ V ((1/2:ℝ) • (y+u)) - (1/(4*η)) • innerSL ℝ (y-u)
∃ 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) ∧ ContDiff ℝ 1 (s y) ∧
(∀ u v w,
(fderiv ℝ (fderiv ℝ (W y)) u v) w =
(1/4:ℝ)*(fderiv ℝ (fderiv ℝ V) ((1/2:ℝ) • (y+u)) v) w + (1/(4*η))*inner ℝ v w ∧
(fderiv ℝ (s y) u v) w = (1/(4*η))*inner ℝ v w -
(1/4:ℝ)*(fderiv ℝ (fderiv ℝ V) ((1/2:ℝ) • (y+u)) v) w) ∧
(∀ u v, (((α:ℝ)+1/η)/4)*‖v‖^2 ≤ (fderiv ℝ (fderiv ℝ (W y)) u v) v) ∧
(∀ u, ‖fderiv ℝ (s y) u‖ ≤ (1/η-(α:ℝ))/4) ∧
∀ a : E, ContDiff ℝ 1 (fun u => s y u a) ∧
MemLp (fun u => s y u a) 2 (S y) ∧
AutoSamplingTheory.TechnicalLemmas.FunctionalInequalities.Poincare.Admissible (S y) (fun u => s y u a) ∧
∀ u, ‖gradient (fun z => s y z a) u‖ ≤ ((1/η-(α:ℝ))/4)*‖a‖ := by
have conditional_derivatives {E : Type u} [NormedAddCommGroup E] [InnerProductSpace ℝ E] [CompleteSpace E]
{V : E → ℝ} (hV : ContDiff ℝ 2 V) (η : ℝ) (y : E) :
let W := fun u : E => V ((1/2:ℝ) • (y+u)) + ‖u-y‖^2/(8*η)
let s := fun u : E => -(1/2:ℝ) • fderiv ℝ V ((1/2:ℝ) • (y+u)) -
(1/(4*η)) • innerSL ℝ (y-u)
ContDiff ℝ 2 W ∧ ContDiff ℝ 1 s ∧
∀ u v w : E,
(fderiv ℝ (fderiv ℝ W) u v) w =
(1/4:ℝ) * (fderiv ℝ (fderiv ℝ V) ((1/2:ℝ) • (y+u)) v) w +
(1/(4*η)) * inner ℝ v w ∧
(fderiv ℝ s u v) w =
(1/(4*η)) * inner ℝ v w -
(1/4:ℝ) * (fderiv ℝ (fderiv ℝ V) ((1/2:ℝ) • (y+u)) v) w := by
let mid := fun u : E => (1/2:ℝ) • (y+u)
let W := fun u : E => V (mid u) + ‖u-y‖^2/(8*η)
let s := fun u : E => -(1/2:ℝ) • fderiv ℝ V (mid u) -
(1/(4*η)) • innerSL ℝ (y-u)
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