Download lean/ComparatorChallenges/OrdinaryElliott.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
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hf download hf://datasets/roseking/openAI-math-dataset/lean/ComparatorChallenges/OrdinaryElliott.lean
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1.54 kB
| import Mathlib | |
| namespace OAI | |
| noncomputable section | |
| open scoped BigOperators | |
| open Filter | |
| namespace OrdinaryCorrelations | |
| def OneBounded (f : ℕ → ℂ) : Prop := ∀ n, ‖f n‖ ≤ 1 | |
| def Multiplicative (f : ℕ → ℂ) : Prop := | |
| ∀ m n : ℕ, 0 < m → 0 < n → m.Coprime n → f (m * n) = f m * f n | |
| def distanceSq (f : ℕ → ℂ) {q : ℕ} (χ : DirichletCharacter ℂ q) | |
| (t X : ℝ) : ℝ := | |
| ∑ p ∈ (Finset.Icc 2 ⌊X⌋₊).filter Nat.Prime, | |
| (1 - (f p * star (χ (p : ZMod q) * | |
| Complex.exp ((t * Real.log (p : ℝ) : ℝ) * Complex.I))).re) / (p : ℝ) | |
| def distance (f : ℕ → ℂ) {q : ℕ} (χ : DirichletCharacter ℂ q) | |
| (t X : ℝ) : ℝ := Real.sqrt (distanceSq f χ t X) | |
| def UniformlyNonpretentious (f : ℕ → ℂ) : Prop := | |
| ∀ (q : ℕ), 0 < q → ∀ χ : DirichletCharacter ℂ q, | |
| Tendsto (fun N : ℕ => sInf ((fun t : ℝ => distance f χ t (N : ℝ)) '' | |
| Set.Icc (-(N : ℝ)) (N : ℝ))) atTop atTop | |
| def shiftAverage (f₁ f₂ : ℕ → ℂ) (h₁ h₂ N : ℕ) : ℂ := | |
| (N : ℂ)⁻¹ * ∑ n ∈ Finset.Icc 1 N, f₁ (n + h₁) * f₂ (n + h₂) | |
| theorem binary_corrected_elliott | |
| (f₁ f₂ : ℕ → ℂ) (hf₁ : OneBounded f₁) (hf₂ : OneBounded f₂) | |
| (hm₁ : Multiplicative f₁) (hm₂ : Multiplicative f₂) | |
| (hNP : UniformlyNonpretentious f₁ ∨ UniformlyNonpretentious f₂) | |
| (h₁ h₂ : ℕ) (hne : h₁ ≠ h₂) : | |
| Tendsto (shiftAverage f₁ f₂ h₁ h₂) atTop (nhds 0) := by | |
| sorry | |
| end OrdinaryCorrelations | |
| end | |
| end OAI | |