test module
Tests.SmoothedPicardHMCPicardCenterMoment
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Complete Lean source
Tests/SmoothedPicardHMCPicardCenterMoment.lean — full module with imports and context
import AutoSamplingTheory.ExampleCases.SmoothedPicardHMC.PicardCenterMoment
open AutoSamplingTheory.ExampleCases.SmoothedPicardHMC
open PicardCenterMoment InnerProductSpace MeasureTheory ProbabilityTheory
open scoped NNReal
#check picard_center_gradient_moment
#print axioms picard_center_gradient_moment
-- A genuine two-node execution at eta > 1/2. The zero-dimensional model makes
-- every phase-state and Gaussian moment exact while still exercising the actual
-- stopped proximal interpreter and both finite Picard layers.
example : ∃ N : EuclideanSpace ℝ (Fin 0) → ℕ,
ApproximateProximalExecution.proximalQuery
(gradient (fun _ : EuclideanSpace ℝ (Fin 0) => (0 : ℝ))) (3 / 4) (1 / 10)
0 (N 0 + 1) 0 = some (0, N 0 + 1) := by
let E := EuclideanSpace ℝ (Fin 0)
let _ : MeasurableSpace E := borel E
have hH : ∀ x v : E,
((1 : ℝ≥0) : ℝ)⁻¹ * ‖v‖ ^ 2 ≤
(fderiv ℝ (fderiv ℝ (fun _ : E => (0 : ℝ))) x v) v ∧
(fderiv ℝ (fderiv ℝ (fun _ : E => (0 : ℝ))) x v) v ≤ ‖v‖ ^ 2 := by
intro x v
have hv : v = 0 := Subsingleton.elim _ _
simp [hv]
obtain ⟨p, N, q, hp, hN, hq, heq, hrun, h0, hz, h1⟩ :=
picard_center_gradient_moment
(E := E) (Ω := Unit) (ι := Fin 2)
(V := fun _ : E => (0 : ℝ)) (κ := 1) (by norm_num) contDiff_const hH
(eta := 3 / 4) (c := 7 / 8) (eps := 1 / 10)
(by norm_num) (by norm_num) (by norm_num) (by norm_num)
(Measure.dirac ()) (fun _ => 0) (fun _ => 0) (by fun_prop) (by fun_prop)
0 (by simp) (fun _ => 1) (by simp) (fun _ _ => 0) (by simp)
(fun _ _ => 1) (M := 0) (S := 2) (by norm_num) (by norm_num)
(by simp) (by simp) (by intro; simp) (by intro; simp) (by intro; norm_num)
have hfirstLayer := h0 (0 : Fin 2)
have hqueryLayer := hz (0 : Fin 2)
have hsecondLayer := h1 (0 : Fin 2)
have _ := hfirstLayer.2
have _ := hqueryLayer.2
have _ := hsecondLayer.1
have _ := hsecondLayer.2
refine ⟨N, ?_⟩
have hh := (hrun (0 : E)).2
simpa only [Subsingleton.elim (q 0) 0] using hh