openAI-math-dataset / lean /OAI /Probability /DilutedSpin /AveragedMatrixSelection.lean
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import OAI.Probability.DilutedSpin.JointGridSelection
import OAI.Probability.DilutedSpin.SpinRootData
namespace OAI
section
section
namespace DilutedSpinGlass.DepthAverage
open Filter
open scoped Topology BigOperators
variable {α : Type} [Fintype α] [DecidableEq α] {L : ℕ} [NeZero L]
/-- At each fixed grid, there are only finitely many depth assignments. Thus
the signed physical identity error can be averaged BEFORE the size limit. -/
theorem tendsto_average_zero (D : (α → Fin L) → Prop)
(F : ℕ → (α → Fin L) → ℝ)
(hF : ∀ q, Tendsto (fun n => F n q) atTop (𝓝 0)) :
Tendsto (fun n => average D (F n)) atTop (𝓝 0) := by
classical
simp only [average_eq]
have hs := tendsto_finsetSum (Finset.univ.filter D) (fun q _ => hF q)
have hh := hs.div_const ((L:ℝ)^Fintype.card α)
simpa only [Finset.sum_const_zero,zero_div] using hh
end DilutedSpinGlass.DepthAverage
namespace DilutedSpinGlass.UniversalDictionary
open _root_.MeasureTheory _root_.OAI.MeasureTheory ProbabilityTheory HeterogeneousMarks PhysicalRoot PrescribedTree ConcreteReservoir Filter Set
open scoped NNReal BigOperators Topology
variable {α : Type} [Fintype α] [DecidableEq α] {L p : ℕ}
/-- The low-increment grid sequence simultaneously has its regular singleton
base case and a vanishing normalized regular-cube matrix error. The old tree,
target shape, labeling and charging denominator may all vary with the complete
depth assignment. This is the actual shared-root signed covariance, not an
absolute coefficient majorant or a diagonal restriction of two averages. -/
theorem joint_grid_averaged_selection (M : Model p) {C H : ℝ} (hC : 0 ≤ C) (hH : 0 ≤ H)
(hθ : ∀ᵐ z ∂M.disorder.toMeasure, ∀ σ, |z.1 σ| ≤ C)
(hh : ∀ᵐ h ∂M.field.toMeasure, |h| ≤ H)
(hθi : Integrable (fun z : InteractionSample p => ‖z.1‖) M.disorder.toMeasure)
(hhi : Integrable (fun h : ℝ => |h|) M.field.toMeasure)
{ε : ℝ} (hε : 0 < ε) :
∃ (Ns : ℕ → ℕ) (us : ℕ → Spec L×ℕ → ℝ), StrictMono Ns ∧
(∀ n i, us n i ∈ Icc (probeLow i.2) (probeHigh i.2)) ∧
(∀ n, ConcreteReservoir.increment (weights L) prior (gridExponents L) direction anchor M (Ns n) (us n) ≤
liminf (pressure M) atTop+ε) ∧
(∀ η : ℝ, 0 < η → ∀ t : ℝ, 0 < t → ∀ᶠ n in atTop,
∀ d ∈ regularSingletonDepths L η,
physicalSingletonEnergy (gridExponents L) M C H (Ns n+1) (us n) d ≤
((L+1:ℕ):ℝ)⁻¹/η+t) ∧
(∀ (D : (α → Fin (L+1)) → Prop)
(S T : (α → Fin (L+1)) → PrescribedTree (L+1))
(a : (Q : α → Fin (L+1)) → (S Q).Leaf)
(k : (α → Fin (L+1)) → ℕ), (∀ Q, 0<k Q) →
∀ (q : (Q : α → Fin (L+1)) → Option (Fin (k Q)) → (T Q).Leaf)
(denom : (α → Fin (L+1)) → ℝ),
Tendsto (fun n => DepthAverage.average D (fun Q =>
|physicalTreeMatrixCovariance (gridExponents L) (S Q) (a Q) M C H
(Ns n+1) (us n) (T Q) (q Q)|/|denom Q|)) atTop (𝓝 0)) := by
obtain ⟨Ns,us,hNs,hus,hinc,henergy,hmatrix⟩ := joint_grid_selection M hC hH hθ hh hθi hhi hε
refine ⟨Ns,us,hNs,hus,hinc,henergy,?_⟩
intro D S T a k hk q denom
apply DepthAverage.tendsto_average_zero
intro Q
have hh := (hmatrix (S Q) (a Q) (k Q) (hk Q) (T Q) (q Q)).abs.div_const |denom Q|
simpa only [abs_zero,zero_div] using hh
end DilutedSpinGlass.UniversalDictionary
end
end
end OAI