openAI-math-dataset / lean /OAI /Analysis /HardSphere /Paths /SkeletonHitConstant.lean
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Add lean source and binary files (part 19)
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import Mathlib
import OAI.Analysis.HardSphere.Paths.LevelList
namespace OAI
section
noncomputable section
open MeasureTheory ProbabilityTheory Set Filter
open scoped ENNReal BigOperators Topology
namespace HardSphere.DyadicSkeleton
lemma occurrenceHit_le (K n : ℕ) (hn : 0 < n) (hnK : 8*n ≤ K)
(d : Fin 2 → ℤ) (hd : ∀j, |d j| ≤ n) (ω ω' : Noise) (R : ℝ) :
occurrenceHit K R (occurrenceList n d ω) (occurrenceList n d ω') ≤
20*∑ u ∈ Finset.range (n/2+1),
nearPotential u (fun j ↦ position ω' j u) (3*R+5) ω := by
have h := oneSideHit_le K n hn hnK d hd ω ω' R
have h' := oneSideHit_le K n hn hnK d hd ω' ω R
simp_rw [nearPotential_swap _ _ ω ω'] at h'
unfold occurrenceHit
linarith
lemma occurrenceSoft_le (K n : ℕ) (hn : 0 < n) (hnK : 8*n ≤ K)
(d : Fin 2 → ℤ) (hd : ∀j, |d j| ≤ n) (ω ω' : Noise) (B : ℝ) (hB : 0 < B) :
occurrenceSoft K B (occurrenceList n d ω) (occurrenceList n d ω') ≤
(10*Real.exp (5/(3*B)))*∑ u ∈ Finset.range (n/2+1),
Real.exp (-twoCopyDistance u (ω,ω')/(3*B)) := by
have h := level_sum_le n hn d hd ω
(fun x ↦ Real.exp (-distanceToList K x (occurrenceList n d ω')/B))
(fun t ↦ Real.exp (5/(3*B))*Real.exp (-twoCopyDistance (min t (n-t)) (ω,ω')/(3*B)))
(fun _ ↦ by positivity) (by
intro t ht x hx
rw [← Real.exp_add]
apply Real.exp_le_exp.mpr
have hdev := distanceToList_deviation_le K n hn hnK d hd ω ω' t ht x hx
have hBp : 0 < 3*B := by positivity
rw [← add_div]
apply (le_div_iff₀ hBp).mpr
have he : (-distanceToList K x (occurrenceList n d ω')/B)*(3*B) =
-3*distanceToList K x (occurrenceList n d ω') := by field_simp
rw [he]
linarith)
have hf := folded_sum_le n
(fun u ↦ Real.exp (-twoCopyDistance u (ω,ω')/(3*B))) (fun _ ↦ (Real.exp_pos _).le)
change occurrenceSoft K B _ _ ≤ _ at h
simp_rw [← Finset.mul_sum] at h
have hpos : 0 < Real.exp (5/(3*B)) := Real.exp_pos _
nlinarith
def skeletonHitConstant : ℝ := hitConstant/720
lemma skeletonHitConstant_pos : 0 < skeletonHitConstant := div_pos hitConstant_pos (by norm_num)
theorem skeleton_hit_moment (K n : ℕ) (hn : 0 < n) (hnK : 8*n ≤ K)
(d : Fin 2 → ℤ) (hd : ∀j, |d j| ≤ n) (R : ℕ) :
(∫⁻ z : Noise × Noise, ENNReal.ofReal (Real.exp
((skeletonHitConstant/(1+(R:ℝ))^2) *
occurrenceHit K (R:ℝ) (occurrenceList n d z.1) (occurrenceList n d z.2)))
∂law.prod law) ≤ 2 := by
have hcoeff : skeletonHitConstant/(1+(R:ℝ))^2*20 ≤
hitConstant/(1+((3*R+5:ℕ):ℝ))^2 := by
have hR : 0 ≤ (R:ℝ) := by positivity
have hden : (1+((3*R+5:ℕ):ℝ))^2 ≤ 36*(1+(R:ℝ))^2 := by
push_cast
nlinarith
calc
_ = hitConstant/(36*(1+(R:ℝ))^2) := by
unfold skeletonHitConstant
field_simp
ring
_ ≤ _ := div_le_div_of_nonneg_left hitConstant_pos.le (by positivity) hden
refine le_trans (lintegral_mono (fun z ↦ ?_)) (two_copy_hit_moment (n/2) (3*R+5))
apply ENNReal.ofReal_le_ofReal
apply Real.exp_le_exp.mpr
have h := occurrenceHit_le K n hn hnK d hd z.1 z.2 (R:ℝ)
have ha : 0 ≤ skeletonHitConstant/(1+(R:ℝ))^2 := by
exact div_nonneg skeletonHitConstant_pos.le (sq_nonneg _)
calc
_ ≤ (skeletonHitConstant/(1+(R:ℝ))^2) *
(20*∑ u ∈ Finset.range (n/2+1),
nearPotential u (fun j ↦ position z.2 j u) (3*(R:ℝ)+5) z.1) :=
mul_le_mul_of_nonneg_left h ha
_ = (skeletonHitConstant/(1+(R:ℝ))^2*20) *
∑ u ∈ Finset.range (n/2+1),
nearPotential u (fun j ↦ position z.2 j u) ((3*R+5:ℕ):ℝ) z.1 := by
push_cast
ring
_ ≤ _ := mul_le_mul_of_nonneg_right hcoeff
(Finset.sum_nonneg (fun u _ ↦ nearPotential_nonneg _ _ _ _))
theorem skeleton_soft_moment (B : ℝ) (hB : 0 < B) :
∃ ξ : ℝ, 0 < ξ ∧ ∀ K n : ℕ, 0 < n → 8*n ≤ K →
∀ d : Fin 2 → ℤ, (∀j, |d j| ≤ n) →
(∫⁻ z : Noise × Noise, ENNReal.ofReal (Real.exp
(ξ*occurrenceSoft K B (occurrenceList n d z.1) (occurrenceList n d z.2)))
∂law.prod law) ≤ 2 := by
obtain ⟨η,hη,hm⟩ := two_copy_soft_moment (3*B) (by positivity)
let C : ℝ := 10*Real.exp (5/(3*B))
have hC : 0 < C := by dsimp [C]; positivity
refine ⟨η/C,div_pos hη hC,fun K n hn hnK d hd ↦ ?_⟩
refine le_trans (lintegral_mono (fun z ↦ ?_)) (hm (n/2))
apply ENNReal.ofReal_le_ofReal
apply Real.exp_le_exp.mpr
calc
_ ≤ (η/C)*(C*∑ u ∈ Finset.range (n/2+1),
Real.exp (-twoCopyDistance u z/(3*B))) :=
mul_le_mul_of_nonneg_left (occurrenceSoft_le K n hn hnK d hd z.1 z.2 B hB)
(div_pos hη hC).le
_ = _ := by field_simp
instance listVertexMeasurableSpace : MeasurableSpace (List Vertex) := ⊤
instance listVertexDiscreteMeasurableSpace : DiscreteMeasurableSpace (List Vertex) := ⟨fun _ ↦ trivial⟩
@[fun_prop] lemma measurable_levelList (n : ℕ) (d : Fin 2 → ℤ) (t : ℕ) :
Measurable (fun ω ↦ levelList n d ω t) := by
cases t with
| zero =>
exact (measurable_of_countable (fun a : Vertex ↦ [a])).comp
(measurable_baseVertex n d 0)
| succ t =>
exact (measurable_of_countable (fun a : Vertex × Vertex ↦ refineStep a.1 a.2)).comp
((measurable_baseVertex n d t).prodMk (measurable_baseVertex n d (t+1)))
@[fun_prop] lemma measurable_occurrenceList (n : ℕ) (d : Fin 2 → ℤ) :
Measurable (occurrenceList n d) := by
have hl (ts : List ℕ) : Measurable (fun ω ↦ ts.flatMap (levelList n d ω)) := by
induction ts with
| nil => exact measurable_const
| cons t ts ih =>
simpa only [List.flatMap_cons,Function.comp_def] using
(measurable_of_countable (fun p : List Vertex × List Vertex ↦ p.1 ++ p.2)).comp
((measurable_levelList n d t).prodMk ih)
exact hl (List.range (n+1))
def skeletonLaw (n : ℕ) (d : Fin 2 → ℤ) : Measure (List Vertex) :=
law.map (occurrenceList n d)
instance skeletonLaw_isProbability (n : ℕ) (d : Fin 2 → ℤ) :
IsProbabilityMeasure (skeletonLaw n d) :=
(Measure.isProbabilityMeasure_map_iff
(measurable_occurrenceList n d).aemeasurable).mpr inferInstance
end HardSphere.DyadicSkeleton
end
end
end OAI