openAI-math-dataset / lean /OAI /NumberTheory /TwoPoint /PublishedInputs.lean
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import OAI.NumberTheory.TwoPoint.Basic
import OAI.NumberTheory.TwoPoint.PretentiousDistance
import Mathlib.MeasureTheory.Integral.IntervalIntegral.Basic
import Mathlib.NumberTheory.DirichletCharacter.Orthogonality
namespace OAI
/-!
# Short-interval estimates for multiplicative functions
The estimates specialize K. Matomäki, M. Radziwiłł and T. Tao,
*An averaged form of Chowla's conjecture*, Algebra & Number Theory 9 (2015),
2167–2196, corrected arXiv:1503.05121v3 (1 March 2022), Theorems 1.3 and 1.7;
DOI 10.2140/ant.2015.9.2167.
The frequency is fixed during the integral. A uniform lower bound on the
squared pretentious distances replaces the infimum in Theorem 1.7.
-/
namespace TwoPointCorrelations
open scoped BigOperators
open MeasureTheory
/-- The usual additive character `e(αn) = exp(2πiαn)`. -/
noncomputable def additiveCharacter (α : ℝ) (n : ℕ) : ℂ :=
Complex.exp (((2 * Real.pi * α * (n : ℝ) : ℝ) : ℂ) * Complex.I)
/-- Sum over the integers `y < n ≤ y+H` (for the nonnegative starting points
used in the integral). -/
noncomputable def shortExponentialSum (b : ℕ → ℂ) (H : ℕ) (α y : ℝ) : ℂ :=
∑ n ∈ Finset.Icc (Nat.floor y + 1) (Nat.floor (y + (H : ℝ))),
b n * additiveCharacter α n
/-- The mean over interval origins, with a single fixed frequency. -/
noncomputable def shortExponentialIntegral (b : ℕ → ℂ) (X H : ℕ) (α : ℝ) : ℝ :=
∫ y in (0 : ℝ)..(X : ℝ), ‖shortExponentialSum b H α y‖
/-- The character-modulus cutoff in corrected MRT Theorem 1.7. -/
noncomputable def mrtModulusCutoff (X H : ℕ) : ℝ :=
min ((Real.log (X : ℝ)) ^ (1 / (125 : ℝ))) ((Real.log (H : ℝ)) ^ (5 : ℕ))
/-- The error shared by MRT Theorems 1.3 and 1.7. -/
noncomputable def mrtShortError (X H : ℕ) : ℝ :=
Real.log (Real.log (H : ℝ)) / Real.log (H : ℝ) +
(Real.log (X : ℝ)) ^ (-1 / (700 : ℝ))
/-- The exact range of lower bounds required by corrected MRT Theorem 1.7. -/
def MRTDistanceLowerBound (b : ℕ → ℂ) (X H : ℕ) (M : ℝ) : Prop :=
∀ (q : ℕ), 0 < q → (q : ℝ) ≤ mrtModulusCutoff X H →
∀ (χ : DirichletCharacter ℂ q) (t : ℝ), |t| ≤ (X : ℝ) →
M ≤ squaredDistance b (characterTwist χ t) X
/-- Natural-cutoff specialization of corrected MRT Theorem 1.7, with an
explicit lower bound `M` in place of the infimum of squared distances. -/
def MRTShortExponentialInput : Prop :=
∃ C : ℝ, 0 < C ∧ ∀ (X H : ℕ), 10 ≤ H → H ≤ X →
∀ (b : ℕ → ℂ), Multiplicative b → OneBounded b →
∀ M : ℝ, MRTDistanceLowerBound b X H M → ∀ α : ℝ,
shortExponentialIntegral b X H α ≤
C * (H : ℝ) * (X : ℝ) * (Real.exp (-M / 20) + mrtShortError X H)
/-- Natural-cutoff Liouville specialization of MRT Theorem 1.3. Endpoint
conventions differ only at a measure-zero set of interval origins. -/
def MRTLiouvilleShortInput : Prop :=
∃ C : ℝ, 0 < C ∧ ∀ (X H : ℕ), 10 ≤ H → H ≤ X → ∀ α : ℝ,
shortExponentialIntegral liouville X H α ≤
C * (H : ℝ) * (X : ℝ) * mrtShortError X H
/-- At a fixed modulus cutoff, uniform nonpretentiousness gives one bound
simultaneously for every character and every permitted finite-prime change. -/
theorem UniformlyNonpretentious.bounded_moduli {f : ℕ → ℂ}
(hfnp : UniformlyNonpretentious f) (hf : OneBounded f)
(P : Finset ℕ) (Q : ℕ) (K : ℝ) :
∀ᶠ N : ℕ in Filter.atTop, ∀ q : ℕ, 0 < q → q ≤ Q →
∀ (χ : DirichletCharacter ℂ q) (b : ℕ → ℂ), OneBounded b →
(∀ p, Nat.Prime p → p ∉ P → b p = f p) →
∀ t : ℝ, |t| ≤ (N : ℝ) → K ≤ squaredDistance b (characterTwist χ t) N := by
classical
let ι := (Σ q : Fin Q, DirichletCharacter ℂ (q.val + 1))
have hfamily := hfnp.finite_family (ι := ι) hf P
(fun i => i.1.val + 1) (fun _ => Nat.succ_pos _) (fun i => i.2) K
filter_upwards [hfamily] with N hN
intro q hq hqQ χ b hb heq t ht
cases q with
| zero => omega
| succ q =>
exact hN ⟨⟨q, Nat.lt_of_succ_le hqQ⟩, χ⟩ b hb heq t ht
/-- Fixing the short interval length leaves only finitely many moduli in the
published theorem. Thus the entire MRT distance hypothesis holds uniformly. -/
theorem UniformlyNonpretentious.eventually_mrt_lower_bound {f : ℕ → ℂ}
(hfnp : UniformlyNonpretentious f) (hf : OneBounded f)
(P : Finset ℕ) (H : ℕ) (M : ℝ) :
∀ᶠ N : ℕ in Filter.atTop, ∀ b : ℕ → ℂ, OneBounded b →
(∀ p, Nat.Prime p → p ∉ P → b p = f p) → MRTDistanceLowerBound b N H M := by
have hfinite := hfnp.bounded_moduli hf P (Nat.ceil ((Real.log (H : ℝ)) ^ (5 : ℕ))) M
filter_upwards [hfinite] with N hN
intro b hb heq q hq hqCutoff χ t ht
have hqQ : q ≤ Nat.ceil ((Real.log (H : ℝ)) ^ (5 : ℕ)) := by
exact_mod_cast hqCutoff.trans ((min_le_right _ _).trans (Nat.le_ceil _))
exact hN q hq hqQ χ b hb heq t ht
/-- The published short-sum estimate immediately gives a bound on the
normalized mean, without moving a frequency supremum into the integral. -/
theorem MRTShortExponentialInput.normalized (hMRT : MRTShortExponentialInput) :
∃ C : ℝ, 0 < C ∧ ∀ (X H : ℕ), 10 ≤ H → H ≤ X →
∀ (b : ℕ → ℂ), Multiplicative b → OneBounded b →
∀ M : ℝ, MRTDistanceLowerBound b X H M → ∀ α : ℝ,
shortExponentialIntegral b X H α / ((H : ℝ) * (X : ℝ)) ≤
C * (Real.exp (-M / 20) + mrtShortError X H) := by
obtain ⟨C, hC, hbound⟩ := hMRT
refine ⟨C, hC, ?_⟩
intro X H hH hX b hmult hbounded M hM α
have hHp : (0 : ℝ) < H := by exact_mod_cast (by omega : 0 < H)
have hXp : (0 : ℝ) < X := by exact_mod_cast (by omega : 0 < X)
apply (div_le_iff₀ (mul_pos hHp hXp)).mpr
convert hbound X H hH hX b hmult hbounded M hM α using 1
ring
/-- A genuine conditional consequence of published MRT Theorem 1.7:
finite local modifications preserve the uniform short-sum estimate at every
fixed short-interval length. The estimate is uniform over all modifications
and frequencies, with the frequency still outside the integral. -/
theorem MRTShortExponentialInput.finite_prime_changes
(hMRT : MRTShortExponentialInput) {f : ℕ → ℂ}
(hfnp : UniformlyNonpretentious f) (hf : OneBounded f) (P : Finset ℕ) :
∃ C : ℝ, 0 < C ∧ ∀ (H : ℕ), 10 ≤ H → ∀ M : ℝ,
∀ᶠ N : ℕ in Filter.atTop, ∀ b : ℕ → ℂ,
Multiplicative b → OneBounded b →
(∀ p, Nat.Prime p → p ∉ P → b p = f p) → ∀ α : ℝ,
shortExponentialIntegral b N H α / ((H : ℝ) * (N : ℝ)) ≤
C * (Real.exp (-M / 20) + mrtShortError N H) := by
obtain ⟨C, hC, hbound⟩ := hMRT.normalized
refine ⟨C, hC, ?_⟩
intro H hH M
filter_upwards [hfnp.eventually_mrt_lower_bound hf P H M,
Filter.eventually_ge_atTop H] with N hN hHN
intro b hmult hb heq α
exact hbound N H hH hHN b hmult hb M (hN b hb heq) α
end TwoPointCorrelations
end OAI