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AlexKontorovich/PrimeNumberTheoremAnd
Source indexedtheorem · leanprover/lean4:v4.32.0

FKS2.theorem_6_alt

PrimeNumberTheoremAnd.IEANTN.FKS2 · PrimeNumberTheoremAnd/IEANTN/FKS2.lean:3853 to 3894

Mathematical statement

Exact Lean statement

@[blueprint
  "fks2-theorem-6"
  (title := "FKS2 Theorem 6")
  (statement := /--
  Let $x_0 > 0$ be chosen such that $\pi(x_0)$ and $\theta(x_0)$ are computable, and let
  $x_1 \geq \max(x_0, 14)$. Let $\{b_i\}_{i=1}^N$ be a finite partition of
  $[\log x_0, \log x_1]$, with $b_1 = \log x_0$ and $b_N = \log x_1$, and suppose that
  $\varepsilon_{\theta,\mathrm{num}}$ gives computable admissible numerical bounds for
  $x = \exp(b_i)$, for each $i=1,\dots,N$.  For $x_1 \leq x_2 \leq x_1 \log x_1$, we define
  $$ \mu_{num}(x_0,x_1,x_2) = \frac{x_0 \log x_1}{\varepsilon_{\theta,num}(x_0) x_1 \log x_0}
    \left|\frac{\pi(x_0) - \Li(x_0)}{x_0/\log x_0} - \frac{\theta(x_0) - x_0}{x_0}\right|$$
  $$ + \frac{\log x_1}{\varepsilon_{\theta,num}(x_1) x_1} \sum_{i=1}^{N-1}
    \varepsilon_{\theta,num}(\exp(b_i))
    \left( \Li(e^{b_{i+1}}) - \Li(e^{b_i}) + \frac{e^{b_i}}{b_i} - \frac{e^{b_{i+1}}}{b_{i+1}}\right)$$
  $$ + \frac{\log x_2}{x_2}
    \left( \Li(x_2) - \frac{x_2}{\log x_2} - \Li(x_1) + \frac{x_1}{\log x_1} \right)$$
  and for $x_2 > x_1 \log x_1$, including the case $x_2 = \infty$, we define
  $$ \mu_{num}(x_0,x_1,x_2) = \frac{x_0 \log x_1}{\varepsilon_{\theta,num}(x_1) x_1 \log x_0}
    \left|\frac{\pi(x_0) - \Li(x_0)}{x_0/\log x_0} - \frac{\theta(x_0) - x_0}{x_0}\right|$$
  $$ + \frac{\log x_1}{\varepsilon_{\theta,num}(x_1) x_1} \sum_{i=1}^{N-1}
    \varepsilon_{\theta,num}(\exp(b_i))
    \left( \Li(e^{b_{i+1}}) - \Li(e^{b_i}) + \frac{e^{b_i}}{b_i} - \frac{e^{b_{i+1}}}{b_{i+1}}\right)$$
  $$ + \frac{1}{\log x_1 + \log\log x_1 - 1}.$$
  Then, for all $x_1 \leq x \leq x_2$ we have
  $$ E_\pi(x) \leq \varepsilon_{\pi,num}(x_1,x_2) :=
    \varepsilon_{\theta,num}(x_1)(1 + \mu_{num}(x_0,x_1,x_2)).$$ -/)
  (proof := /-- This follows by combining the three substeps. -/)
  (latexEnv := "theorem")
  (discussion := 718)]
theorem theorem_6_alt {x₀ x₁ : ℝ} (h : x₁ ≥ max x₀ 14)
  {N : ℕ} (b : Fin (N + 1) → ℝ) (hmono : Monotone b)
  (h_b_start : b 0 = log x₀)
  (h_b_end : b (Fin.last N) = log x₁)
  (εθ_num : ℝ → ℝ)
  (h_εθ_num : ∀ i : Fin (N+1), Eθ.numericalBound (exp (b i)) εθ_num) (x : ℝ) (hx₁ : x₁ ≤ x) (hx₀ : x₀ ≥ 2) :
  Eπ x ≤ εθ_num x₁ * (1 + μ_num_2 b εθ_num x₀ x₁)

Complete declaration

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Full Lean sourceLean 4
@[blueprint  "fks2-theorem-6"  (title := "FKS2 Theorem 6")  (statement := /--  Let $x_0 > 0$ be chosen such that $\pi(x_0)$ and $\theta(x_0)$ are computable, and let  $x_1 \geq \max(x_0, 14)$. Let $\{b_i\}_{i=1}^N$ be a finite partition of  $[\log x_0, \log x_1]$, with $b_1 = \log x_0$ and $b_N = \log x_1$, and suppose that  $\varepsilon_{\theta,\mathrm{num}}$ gives computable admissible numerical bounds for  $x = \exp(b_i)$, for each $i=1,\dots,N$.  For $x_1 \leq x_2 \leq x_1 \log x_1$, we define  $$ \mu_{num}(x_0,x_1,x_2) = \frac{x_0 \log x_1}{\varepsilon_{\theta,num}(x_0) x_1 \log x_0}    \left|\frac{\pi(x_0) - \Li(x_0)}{x_0/\log x_0} - \frac{\theta(x_0) - x_0}{x_0}\right|$$  $$ + \frac{\log x_1}{\varepsilon_{\theta,num}(x_1) x_1} \sum_{i=1}^{N-1}    \varepsilon_{\theta,num}(\exp(b_i))    \left( \Li(e^{b_{i+1}}) - \Li(e^{b_i}) + \frac{e^{b_i}}{b_i} - \frac{e^{b_{i+1}}}{b_{i+1}}\right)$$  $$ + \frac{\log x_2}{x_2}    \left( \Li(x_2) - \frac{x_2}{\log x_2} - \Li(x_1) + \frac{x_1}{\log x_1} \right)$$  and for $x_2 > x_1 \log x_1$, including the case $x_2 = \infty$, we define  $$ \mu_{num}(x_0,x_1,x_2) = \frac{x_0 \log x_1}{\varepsilon_{\theta,num}(x_1) x_1 \log x_0}    \left|\frac{\pi(x_0) - \Li(x_0)}{x_0/\log x_0} - \frac{\theta(x_0) - x_0}{x_0}\right|$$  $$ + \frac{\log x_1}{\varepsilon_{\theta,num}(x_1) x_1} \sum_{i=1}^{N-1}    \varepsilon_{\theta,num}(\exp(b_i))    \left( \Li(e^{b_{i+1}}) - \Li(e^{b_i}) + \frac{e^{b_i}}{b_i} - \frac{e^{b_{i+1}}}{b_{i+1}}\right)$$  $$ + \frac{1}{\log x_1 + \log\log x_1 - 1}.$$  Then, for all $x_1 \leq x \leq x_2$ we have  $$ E_\pi(x) \leq \varepsilon_{\pi,num}(x_1,x_2) :=    \varepsilon_{\theta,num}(x_1)(1 + \mu_{num}(x_0,x_1,x_2)).$$ -/)  (proof := /-- This follows by combining the three substeps. -/)  (latexEnv := "theorem")  (discussion := 718)]theorem theorem_6_alt {x₀ x₁ : } (h : x₁  max x₀ 14)  {N : } (b : Fin (N + 1)  ) (hmono : Monotone b)  (h_b_start : b 0 = log x₀)  (h_b_end : b (Fin.last N) = log x₁)  (εθ_num :   )  (h_εθ_num :  i : Fin (N+1), Eθ.numericalBound (exp (b i)) εθ_num) (x : ) (hx₁ : x₁  x) (hx₀ : x₀  2) :  Eπ x  εθ_num x₁ * (1 + μ_num_2 b εθ_num x₀ x₁) := by  have h6 := theorem_6 (⊤ : EReal) h b hmono h_b_start h_b_end εθ_num h_εθ_num x hx₁ le_top hx₀  suffices hsuff : μ_num b εθ_num x₀ x₁ (⊤ : EReal) = μ_num_2 b εθ_num x₀ x₁ by    have heq : επ_num b εθ_num x₀ x₁ ⊤ = εθ_num x₁ * (1 + μ_num_2 b εθ_num x₀ x₁) := by      dsimp [επ_num]; rw [hsuff]    linarith  dsimp [μ_num]; rfl