openAI-math-dataset / lean /OAI /Probability /SKGap /Stability /DiagonalStability.lean
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import Mathlib
import OAI.Probability.SKGap.Stability.RootDiagonalTransfer
import OAI.Probability.SKGap.Matrix.GOESubmatrix
namespace OAI
section
noncomputable section
namespace SKGap.ObservationBridge
open Matrix Real Set MeasureTheory ProbabilityTheory Filter
open scoped BigOperators ENNReal Topology
lemma absorb_linear_exponential {b : ℝ} (hb : 0 < b) :
∃ N : ℕ, ∀ n ≥ N,
2*(n:ℝ)*exp (-b*(n:ℝ)) ≤ exp (-(b/2)*(n:ℝ)) := by
obtain ⟨N,hN⟩ := exists_nat_ge (16/b^2)
refine ⟨N,fun n hn=>?_⟩
have hnR : 16/b^2 ≤ (n:ℝ) := hN.trans (Nat.cast_le.mpr hn)
have hn0 : 0 ≤ (n:ℝ) := Nat.cast_nonneg _
have hbn : 16 ≤ (n:ℝ)*b^2 := (div_le_iff₀ (sq_pos_of_pos hb)).mp hnR
have hmul := mul_le_mul_of_nonneg_right hbn hn0
have he := Real.pow_div_factorial_le_exp ((b/2)*(n:ℝ)) (by positivity) 2
norm_num only [Nat.factorial_succ,Nat.factorial_zero,Nat.cast_mul,Nat.cast_one,Nat.cast_ofNat] at he
have hhead : 2*(n:ℝ) ≤ exp ((b/2)*(n:ℝ)) := by nlinarith
calc
_ ≤ exp ((b/2)*(n:ℝ))*exp (-b*(n:ℝ)) :=
mul_le_mul_of_nonneg_right hhead (exp_pos _).le
_ = _ := by rw [← exp_add]; congr 1; ring
lemma shifted_goe_diagonal_tail {j δ : ℝ} (hj : 0 < j) (hδ : 0 < δ)
{n : ℕ} (hn : 0 < n) (hmean : j/(n:ℝ) ≤ δ/2) :
(gaussianCoordinates (MatrixCoordinates (Fin n))).real
{g | ∃ i, δ < |plantedInteraction j (goeMatrix (j/(n:ℝ)) g) i i|} ≤
2*(n:ℝ)*exp (-(δ^2/(32*j))*(n:ℝ)) := by
have hr : 0 < j/(n:ℝ) := div_pos hj (Nat.cast_pos.mpr hn)
have hs : {g : MatrixCoordinates (Fin n)→ℝ | ∃ i,
δ < |plantedInteraction j (goeMatrix (j/(n:ℝ)) g) i i|} ⊆
{g | ∃ i, δ/2 < |goeMatrix (j/(n:ℝ)) g i i|} := by
rintro g ⟨i,hi⟩
refine ⟨i,?_⟩
have he : plantedInteraction j (goeMatrix (j/(n:ℝ)) g) i i =
goeMatrix (j/(n:ℝ)) g i i+j/(n:ℝ) := by
simp [plantedInteraction,Matrix.vecMulVec]
rw [he] at hi
have hh := abs_add_le (goeMatrix (j/(n:ℝ)) g i i) (j/(n:ℝ))
rw [abs_of_pos hr] at hh
linarith
apply (measureReal_mono hs (measure_ne_top _ _)).trans
have hh := goe_diagonal_tail (ι:=Fin n) hr (half_pos hδ).le
have he : -(δ/2)^2/(8*(j/(n:ℝ))) = -(δ^2/(32*j))*(n:ℝ) := by
field_simp
ring
simpa only [Fintype.card_fin,he] using hh
def erasedPlantedRootBad (n : ℕ) (j A K ε c ρ t : ℝ) :
Set ((MatrixCoordinates (Fin n)→ℝ) × (Fin n→ℝ)) :=
{p | (∀ z : Field n, vectorNorm
(eraseDiagonal (plantedInteraction j (goeMatrix (j/(n:ℝ)) p.1))*ᵥz) ≤ K*vectorNorm z) ∧
rootBad j A ε c ρ (eraseDiagonal (plantedInteraction j (goeMatrix (j/(n:ℝ)) p.1)))
(fun i=>t+p.2 i)}
lemma erased_planted_probability {n : ℕ} {j A K ε c ρ δ a t : ℝ}
(hj : 0 < j) (hA : 0 ≤ A) (hδ : 0 < δ) (hn : 0 < n)
(hδρ : δ ≤ ρ/2) (hδc : A*δ ≤ c/2) (hmean : j/(n:ℝ) ≤ δ/2)
(hp : ((gaussianCoordinates (MatrixCoordinates (Fin n))).prod
(Measure.pi (fun _ : Fin n=>gaussianReal 0 t.toNNReal))).real
(plantedRootBad n j A K ε c ρ t) ≤ exp (-a*(n:ℝ))) :
((gaussianCoordinates (MatrixCoordinates (Fin n))).prod
(Measure.pi (fun _ : Fin n=>gaussianReal 0 t.toNNReal))).real
(erasedPlantedRootBad n j A (K-δ) ε (c/2) (ρ/2) t) ≤
exp (-a*(n:ℝ))+2*(n:ℝ)*exp (-(δ^2/(32*j))*(n:ℝ)) := by
let μ := gaussianCoordinates (MatrixCoordinates (Fin n))
let ν := Measure.pi (fun _ : Fin n=>gaussianReal 0 t.toNNReal)
let D := {g : MatrixCoordinates (Fin n)→ℝ | ∃ i,
δ < |plantedInteraction j (goeMatrix (j/(n:ℝ)) g) i i|}
have hs : erasedPlantedRootBad n j A (K-δ) ε (c/2) (ρ/2) t ⊆
plantedRootBad n j A K ε c ρ t ∪ (D ×ˢ (Set.univ : Set (Fin n→ℝ))) := by
intro p hp
by_cases hd : p.1∈D
· exact Or.inr ⟨hd,mem_univ _⟩
· apply Or.inl
exact erased_planted_witness hA hδ.le hδρ hδc t p.1 p.2
(fun i=>le_of_not_gt (fun hi=>hd ⟨i,hi⟩)) hp.1 hp.2
have hD : (μ.prod ν).real (D ×ˢ (Set.univ : Set (Fin n→ℝ))) ≤
2*(n:ℝ)*exp (-(δ^2/(32*j))*(n:ℝ)) := by
rw [measureReal_prod_prod]
simp only [Measure.real,measure_univ,ENNReal.toReal_one,mul_one]
exact shifted_goe_diagonal_tail hj hδ hn hmean
exact (measureReal_mono hs (measure_ne_top _ _)).trans
((measureReal_union_le _ _).trans (add_le_add hp hD))
theorem zero_diagonal_fixed_time_root_stability {j T : ℝ}
(hj : 0 < j) (hj1 : j < 1) (hT : 0 < T) :
∃ A K ε c ρ a : ℝ, 1 < A ∧ 2*sqrt j < K ∧ sqrt j*A < 1 ∧
0 < ε ∧ 0 < c ∧ 0 < ρ ∧ 0 < a ∧ ∃ N : ℕ, 0 < N ∧
∀ n ≥ N, ∀ t∈Icc 0 T,
((gaussianCoordinates (MatrixCoordinates (Fin n))).prod
(Measure.pi (fun _ : Fin n=>gaussianReal 0 t.toNNReal))).real
(erasedPlantedRootBad n j A K ε c ρ t) ≤ exp (-a*(n:ℝ)) := by
obtain ⟨A,K,ε,c,ρ,a,hA,hK,hsub,hε,hc,hρ,ha,N,hN,hp⟩ :=
planted_fixed_time_root_stability hj hj1 hT
let δ := min ((K-2*sqrt j)/2) (min (ρ/2) (c/(2*A)))
have hA0 : 0 < A := zero_lt_one.trans hA
have hδ : 0 < δ := lt_min (by linarith) (lt_min (half_pos hρ) (by positivity))
have hδK : δ ≤ (K-2*sqrt j)/2 := min_le_left _ _
have hδρ : δ ≤ ρ/2 := (min_le_right _ _).trans (min_le_left _ _)
have hδc' : δ ≤ c/(2*A) := (min_le_right _ _).trans (min_le_right _ _)
have hδc : A*δ ≤ c/2 := by
have hh := (le_div_iff₀ (by positivity : 0 < 2*A)).mp hδc'
nlinarith
let b := δ^2/(32*j)
have hb : 0 < b := by dsimp [b]; positivity
obtain ⟨Nd,hNd⟩ := absorb_linear_exponential hb
obtain ⟨Nm,hNm⟩ := exists_nat_ge (2*j/δ)
obtain ⟨r,hr,Nr,hNr⟩ := two_exponential_tails ha (half_pos hb)
refine ⟨A,K-δ,ε,c/2,ρ/2,r,hA,by linarith,hsub,hε,half_pos hc,half_pos hρ,hr,
max N (max Nd (max Nm Nr)),hN.trans_le (le_max_left _ _),?_⟩
intro n hn t ht
have hnN : N ≤ n := (le_max_left _ _).trans hn
have hnD : Nd ≤ n := (le_max_left _ _).trans ((le_max_right _ _).trans hn)
have hnM : Nm ≤ n := (le_max_left _ _).trans
((le_max_right _ _).trans ((le_max_right _ _).trans hn))
have hnR : Nr ≤ n := (le_max_right _ _).trans
((le_max_right _ _).trans ((le_max_right _ _).trans hn))
have hn0 : 0 < n := hN.trans_le hnN
have hmean : j/(n:ℝ) ≤ δ/2 := by
have hh := (div_le_iff₀ hδ).mp (hNm.trans (Nat.cast_le.mpr hnM))
apply (div_le_iff₀ (Nat.cast_pos.mpr hn0)).mpr
nlinarith
have hh := erased_planted_probability hj hA0.le hδ hn0 hδρ hδc hmean (hp n hnN t ht)
apply hh.trans
apply (add_le_add_right (hNd n hnD) _).trans
exact (by nlinarith only [exp_pos (-a*(n:ℝ))] :
exp (-a*(n:ℝ))+exp (-(b/2)*(n:ℝ)) ≤
4*exp (-a*(n:ℝ))+exp (-(b/2)*(n:ℝ))).trans (hNr n hnR)
end SKGap.ObservationBridge
end
end
end OAI