openAI-math-dataset / lean /OAI /Probability /SKBarriers /Scalar /ScalarCompactAverage.lean
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Add lean source and binary files (part 127)
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import Mathlib
import OAI.Probability.SKBarriers.Scalar.ScalarHierarchyAverageAlgebra
import OAI.Probability.SKBarriers.Hierarchy.HierarchyScoreGeneral
import OAI.Probability.SKBarriers.Scalar.ScalarLevelRepresentation
namespace OAI
section
noncomputable section
open scoped BigOperators NNReal Topology
open MeasureTheory ProbabilityTheory Filter Set
namespace SK.Analytic
attribute [local instance 2000] parameterNormedGroup parameterNormedSpace
theorem scalarPath_square_bound_convex (n : ℕ) (m v : Fin n → ℝ)
(hm : ∀ i, m i∈Icc (0:ℝ) 1) (hmono : Monotone m)
{f : ℝ → ℝ} (hf : BoundedDerivs f) (hc : ScalarSpinConvex f) :
(∫ z, (coordinateLinear n v z)^2 ∂hierarchyPathLaw n m
(fun z => f (coordinateLinear n v z)) 0) ≤
2*(∑ i, (v i)^2)+4*(∑ i, (v i)^2)^2 := by
have H := hierarchy_square_bound n m hm hmono (fun z => f (coordinateLinear n v z))
(hf.compCLM (coordinateLinear n v)) (fun i => |v i|) (fun i => abs_nonneg _)
(fun z i => by
rw [(scalar_comp_directional hf (coordinateLinear n v) z (coordinateAxis n i)).1,
coordinateLinear_coordinateAxis,abs_mul]
exact (mul_le_mul_of_nonneg_right (hc.bounds _).1 (abs_nonneg _)).trans_eq (one_mul _)) 0 v
have he : (∑ i, |v i| * |v i|) = ∑ i, (v i)^2 :=
Finset.sum_congr rfl (fun i _ => by rw [← sq_abs]; ring)
rw [he] at H
nlinarith
theorem scalarHierarchyAverage_compact_lower (n : ℕ) (m v : Fin n → ℝ)
(hm : ∀ i, m i∈Icc (0:ℝ) 1) (hmono : Monotone m)
{f g : ℝ → ℝ} (hf : BoundedDerivs f) (hc : ScalarSpinConvex f)
(hg : BoundedScalar g) {T c : ℝ} (hT : 0≤T) (hV : ∑ i, (v i)^2 ≤ T)
(hc0 : 0≤c) (hg0 : ∀ x,0≤g x)
(hgc : ∀ x, |x|≤1+2*T+4*T^2 → c≤g x) :
c/2≤ scalarHierarchyAverage n m v f g 0 := by
let μ := hierarchyPathLaw n m (fun z => f (coordinateLinear n v z)) 0
let L := coordinateLinear n v
let M := 2*T+4*T^2
let R := 1+M
have hM : 0≤M := by dsimp [M]; positivity
have hR : 0<R := by dsimp [R]; positivity
have hR2 : 2*M≤R^2 := by dsimp only [R]; nlinarith [sq_nonneg M]
have hRdef : 1+2*T+4*T^2=R := by dsimp [R,M]; ring
have he (x : ℝ) : c*(1-x^2/R^2)≤g x := by
by_cases hx : |x|≤R
· have H : 1-x^2/R^2≤1 := sub_le_self _ (div_nonneg (sq_nonneg _) (sq_nonneg _))
exact (mul_le_of_le_one_right hc0 H).trans (hgc x (hRdef ▸ hx))
· have hsq : R^2≤x^2 := by
simpa only [sq_abs] using (sq_le_sq₀ hR.le (abs_nonneg x)).mpr (le_of_not_ge hx)
have hdiv : 1≤x^2/R^2 := (one_le_div (sq_pos_of_pos hR)).mpr hsq
exact (mul_nonpos_of_nonneg_of_nonpos hc0 (by linarith)).trans (hg0 x)
have hF : BoundedDerivs (fun z => f (L z)) := hf.compCLM L
let := hierarchyPathLaw_probability n m _ hF 0
have hi : Integrable (fun z => (L z)^2) μ := by
rw [show μ=(fiberGaussian n 0).tilted (hierarchyPathLogDensity n m (fun z => f (L z))) from
hierarchyPathLaw_eq_tilted n m _ hF 0]
exact ((HasExpGrowth.linear L).pow 2).integrable_tilted_fiberGaussian n _
(hierarchyPathLogDensity_regular n m hF) (L.continuous.pow 2) 0
have hig : Integrable (fun z => g (L z)) μ := by
obtain ⟨B,hB,hb⟩ := hg.bounded
exact hierarchyPathLaw_integrable n m _ _ hF (hg.continuous.comp L.continuous)
(C:=B) (fun z => by simpa only [Real.norm_eq_abs,Function.comp_apply] using hb (L z)) 0
have H := integral_mono (((integrable_const (1:ℝ)).sub (hi.div_const (R^2))).const_mul c) hig
(fun z => he (L z))
change (∫ z, c*(1-(L z)^2/R^2) ∂μ)≤∫ z,g (L z) ∂μ at H
simp only [integral_const_mul,integral_sub (integrable_const _) (hi.div_const _),
integral_div,integral_const,probReal_univ,smul_eq_mul,one_mul] at H
have hs : 0≤∑ i,(v i)^2 := Finset.sum_nonneg (fun _ _ => sq_nonneg _)
have HM : (∫ z, (L z)^2 ∂μ)≤M := (scalarPath_square_bound_convex n m v hm hmono hf hc).trans (by
dsimp only [M]; nlinarith [sq_le_sq₀ hs hT |>.mpr hV])
have Hd : (∫ z, (L z)^2 ∂μ)/R^2≤1/2 := (div_le_iff₀ (sq_pos_of_pos hR)).mpr (by linarith)
rw [scalarHierarchyAverage_integral hf hg]
simp only [zero_add]
change c/2≤∫ z,g (L z) ∂μ
nlinarith [mul_le_mul_of_nonneg_left Hd hc0]
end SK.Analytic
end
end
end OAI