AutoSamplingTheory.ExampleCases.ProximalBPS.GaussianReflection
Read the mathematical statements and proofs in order
1 named declarations scanned from AutoSamplingTheory/ExampleCases/ProximalBPS/GaussianReflection.lean.
Declarations
theorem AutoSamplingTheory.ExampleCases.ProximalBPS.GaussianReflection.reflection_preserves_augmentation Partial Not mapped
Read the complete mathematical statement and proof, with Lean below each
- Reflecting the auxiliary point through the position is involutive and preserves the generative Gaussian augmentation. All maps used in the pushforward calculation are proved measurable. The positive-scale hypothesis retains the paper's sampling regime; the reflection algebra itself is scale-independent. Intended consumers are the auxiliary update in Section 3.1 and, after a separate `L²` adapter, the reflection operator in Appendix B.
theorem reflection_preserves_augmentation (μ : Measure E) [IsProbabilityMeasure μ]
(η : ℝ) (_hη : 0 < η) :
Function.Involutive (fun p : E × E => (p.1, (2 : ℝ) • p.1 - p.2)) ∧
Measure.map (fun p : E × E => (p.1, (2 : ℝ) • p.1 - p.2))
(Measure.map (fun p : E × E => (p.1, p.1 + Real.sqrt η • p.2))
(μ.prod (stdGaussian E))) =
Measure.map (fun p : E × E => (p.1, p.1 + Real.sqrt η • p.2))
(μ.prod (stdGaussian E)) := by
let Φ : E × E → E × E := fun p => (p.1, p.1 + Real.sqrt η • p.2)
let R : E × E → E × E := fun p => (p.1, (2 : ℝ) • p.1 - p.2)
let S : E × E → E × E := Prod.map id (fun z => -z)
have hΦ : Measurable Φ := by fun_prop
have hR : Measurable R := by fun_prop
have hS : Measurable S := by fun_prop
have hneg : Measure.map (fun z : E => -z) (stdGaussian E) = stdGaussian E := by
simpa using (stdGaussian_map (LinearIsometryEquiv.neg ℝ (E := E)))
have hprod : Measure.map S (μ.prod (stdGaussian E)) = μ.prod (stdGaussian E) := by
dsimp [S]
rw [← Measure.map_prod_map μ (stdGaussian E) measurable_id (by fun_prop),
hneg, Measure.map_id]
have hcomp : R ∘ Φ = Φ ∘ S := by
funext p
apply Prod.ext
· rfl
· dsimp [R, Φ, S]
simp only [smul_neg, two_smul]
abel
constructor
· intro p
apply Prod.ext
· rfl
· exact sub_sub_cancel ((2 : ℝ) • p.1) p.2
· change Measure.map R (Measure.map Φ (μ.prod (stdGaussian E))) =
Measure.map Φ (μ.prod (stdGaussian E))
rw [Measure.map_map hR hΦ, hcomp, ← Measure.map_map hΦ hS, hprod]
end AutoSamplingTheory.ExampleCases.ProximalBPS.GaussianReflection
AutoSamplingTheory/ExampleCases/ProximalBPS/GaussianReflection.lean:33published source at 0e31a3cda412