roseking's picture
Add lean source and binary files (part 118)
180bc03 verified
Raw History Blame Contribute Delete
6.71 kB
import OAI.NumberTheory.TwoPoint.Bounds.ActualPaddingIntegerBounds
import OAI.NumberTheory.TwoPoint.Bounds.CoordinateProjection
import OAI.NumberTheory.TwoPoint.Bounds.NonnegativeProgressionAverage
namespace OAI
/-! Test the retained graph against bounded arithmetic functions. The
norm average uses the truncated integer padding estimate. -/
namespace TwoPointCorrelations
open Finset Filter
open scoped Classical
lemma norm_inner_operator_le {V : Type*} [Fintype V]
(T : EuclideanSpace ℂ V →L[ℂ] EuclideanSpace ℂ V) (v : EuclideanSpace ℂ V) :
‖inner ℂ v (T v)‖ ≤ ‖T‖ * ‖v‖ ^ 2 := by
calc
_ ≤ ‖v‖ * ‖T v‖ := norm_inner_le_norm _ _
_ ≤ ‖v‖ * (‖T‖ * ‖v‖) :=
mul_le_mul_of_nonneg_left (T.le_opNorm v) (norm_nonneg _)
_ = _ := by ring
theorem FiniteLaw.average_operator_test {Ω V : Type*} [Fintype Ω] [Fintype V]
(μ : FiniteLaw Ω) (T : Ω → EuclideanSpace ℂ V →L[ℂ] EuclideanSpace ℂ V)
(v : Ω → EuclideanSpace ℂ V) (B D Z δ : ℝ) (hB : 0 ≤ B) (hD : 0 ≤ D)
(hv : μ.average (fun x => ‖v x‖ ^ 2) ≤ Z)
(hbad : μ.probability (fun x => B < ‖T x‖) ≤ δ)
(hbound : ∀ x, B < ‖T x‖ → ‖inner ℂ (v x) (T x (v x))‖ ≤ D) :
μ.average (fun x => ‖inner ℂ (v x) (T x (v x))‖) ≤ B * Z + D * δ := by
have hp (x : Ω) : ‖inner ℂ (v x) (T x (v x))‖ ≤
B * ‖v x‖ ^ 2 + D * (if B < ‖T x‖ then 1 else 0) := by
by_cases hx : B < ‖T x‖
· rw [ite_eq_left hx, mul_one]
exact (hbound x hx).trans (le_add_of_nonneg_left (mul_nonneg hB (sq_nonneg _)))
· rw [ite_eq_right hx, mul_zero, add_zero]
exact (norm_inner_operator_le (T x) (v x)).trans
(mul_le_mul_of_nonneg_right (le_of_not_gt hx) (sq_nonneg _))
calc
_ ≤ μ.average (fun x => B * ‖v x‖ ^ 2 + D * (if B < ‖T x‖ then 1 else 0)) :=
μ.average_mono hp
_ = B * μ.average (fun x => ‖v x‖ ^ 2) +
D * μ.probability (fun x => B < ‖T x‖) := by
simp only [FiniteLaw.average, FiniteLaw.probability, mul_add, sum_add_distrib]
congr 1 <;> rw [mul_sum] <;> apply sum_congr rfl <;> intro x _ <;> ring
_ ≤ _ := add_le_add (mul_le_mul_of_nonneg_left hv hB)
(mul_le_mul_of_nonneg_left hbad hD)
noncomputable def paddingTestVector {V : Type*} (Q : Finset ℕ) (L : ℝ)
(site : V → ℤ) (f : ℤ → ℂ) : EuclideanSpace ℂ V :=
WithLp.toLp 2 (fun i => if actualPaddingDegreeCut Q L (site i) then
(actualPaddingVertex Q (site i) : ℂ) * f (site i) else 0)
lemma paddingTestVector_norm_sq_le {V : Type*} [Fintype V]
(Q : Finset ℕ) (L : ℝ) (site : V → ℤ) (f : ℤ → ℂ) (hf : ∀ n, ‖f n‖ ≤ 1) :
‖paddingTestVector Q L site f‖ ^ 2 ≤
∑ i, (actualPaddingVertex Q (site i)) ^ 2 *
(if actualPaddingDegreeCut Q L (site i) then 1 else 0) := by
rw [EuclideanSpace.norm_sq_eq]
apply sum_le_sum
intro i _
by_cases hi : actualPaddingDegreeCut Q L (site i)
· simp only [paddingTestVector, WithLp.ofLp_toLp, ite_eq_left hi, norm_mul,
Complex.norm_real, Real.norm_eq_abs, abs_of_pos (actualPaddingVertex_pos Q _), mul_one]
apply pow_le_pow_left₀
(mul_nonneg (actualPaddingVertex_pos Q (site i)).le (norm_nonneg _)) _ 2
simpa only [mul_one] using
mul_le_mul_of_nonneg_left (hf (site i)) (actualPaddingVertex_pos Q (site i)).le
· simp [paddingTestVector, hi]
theorem BravermanDepth22Input.eventually_padding_test_norm
(hBr : BravermanDepth22Input) :
∃ A : ℕ, 1000 ≤ A ∧ ∀ᶠ L : ℝ in atTop,
∀ (Q : Finset ℕ), (∀ p ∈ Q, p.Prime) →
(∀ p ∈ Q, (p : ℝ) ≤ Real.exp L) →
∀ {V : Type*} [Fintype V] (site : V → ℤ) (f : ℤ → ℂ), (∀ n, ‖f n‖ ≤ 1) →
∀ a N : ℕ, Real.exp (L ^ A / 2) ≤ (N : ℝ) →
uniformAverage (fun x : Fin N => ‖paddingTestVector Q L
(fun i => ((a + x.val : ℕ) : ℤ) + site i) f‖ ^ 2) ≤
2 * Fintype.card V * paddingTiltNormalizer Q := by
obtain ⟨A, hA, hb⟩ := hBr.eventually_actual_padding_weight_sum
refine ⟨A, hA, ?_⟩
filter_upwards [hb] with L hb
intro Q hQ hupper V _ site f hf a N hN
classical
calc
_ ≤ uniformAverage (fun x : Fin N => ∑ i : V,
(actualPaddingVertex Q (((a + x.val : ℕ) : ℤ) + site i)) ^ 2 *
if actualPaddingDegreeCut Q L (((a + x.val : ℕ) : ℤ) + site i) then 1 else 0) := by
unfold uniformAverage
apply div_le_div_of_nonneg_right _ (Nat.cast_nonneg _)
exact sum_le_sum (fun x _ => paddingTestVector_norm_sq_le Q L _ f hf)
_ = ∑ i : V, uniformAverage (fun x : Fin N =>
(actualPaddingVertex Q (((a + x.val : ℕ) : ℤ) + site i)) ^ 2 *
if actualPaddingDegreeCut Q L (((a + x.val : ℕ) : ℤ) + site i) then 1 else 0) := by
unfold uniformAverage
rw [sum_comm, sum_div]
_ ≤ _ := by simpa only [card_univ] using hb Q hQ hupper univ site a N hN
theorem BravermanDepth22Input.eventually_padding_test_progression_norm
(hBr : BravermanDepth22Input) :
∃ A : ℕ, 1000 ≤ A ∧ ∀ᶠ L : ℝ in atTop,
∀ (Q : Finset ℕ), (∀ p ∈ Q, p.Prime) →
(∀ p ∈ Q, (p : ℝ) ≤ Real.exp L) →
∀ {V : Type*} [Fintype V] (site : V → ℤ) (f : ℤ → ℂ), (∀ n, ‖f n‖ ≤ 1) →
∀ a l N : ℕ, 0 < l → Real.exp (L ^ A / 2) ≤ (N : ℝ) →
uniformAverage (fun x : Fin N => ‖paddingTestVector Q L
(fun i => ((a + l * x.val : ℕ) : ℤ) + site i) f‖ ^ 2) ≤
(l : ℝ) * (2 * Fintype.card V * paddingTiltNormalizer Q) := by
obtain ⟨A, hA, hb⟩ := hBr.eventually_padding_test_norm
refine ⟨A, hA, ?_⟩
filter_upwards [hb] with L hb
intro Q hQ hupper V _ site f hf a l N hl hN
have hlr : (1 : ℝ) ≤ l := by exact_mod_cast hl
have hNl : Real.exp (L ^ A / 2) ≤ (l * N : ℕ) := by
rw [Nat.cast_mul]
exact hN.trans (by nlinarith [Nat.cast_nonneg (α := ℝ) N])
have hp := uniformAverage_progression_le
(fun n => ‖paddingTestVector Q L (fun i => (n : ℤ) + site i) f‖ ^ 2)
(fun n => sq_nonneg _) a l N hl
exact hp.trans (mul_le_mul_of_nonneg_left
(hb Q hQ hupper site f hf a (l * N) hNl) (Nat.cast_nonneg _))
lemma projected_paddingTestVector_norm_sq_le {V : Type*} [Fintype V] [DecidableEq V]
(Q : Finset ℕ) (L : ℝ) (site : V → ℤ) (f : ℤ → ℂ)
(keep : V → Prop) :
‖coordinateProjection keep (paddingTestVector Q L site f)‖ ^ 2 ≤
‖paddingTestVector Q L site f‖ ^ 2 :=
(sq_le_sq₀ (norm_nonneg _) (norm_nonneg _)).mpr (coordinateProjection_norm_le _ _)
end TwoPointCorrelations
end OAI