Prime Number Theorem And ...

9 Secondary explicit estimates

9.1 Definitions

In this section we define the basic types of secondary estimates we will work with in the project. Key references:

FKS1: Fiori–Kadiri–Swidninsky arXiv:2204.02588

FKS2: Fiori–Kadiri–Swidninsky arXiv:2206.12557

Definition 9.1.1 pi
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\(\pi (x)\) is the number of primes less than or equal to \(x\).

Definition 9.1.2 li and Li
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\(\mathrm{li}(x) = \int _0^x \frac{dt}{\log t}\) and \(\mathrm{Li}(x) = \int _2^x \frac{dt}{\log t}\).

Definition 9.1.3 theta
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\(\theta (x) = \sum _{p \leq x} \log p\) where the sum is over primes \(p\).

Definition 9.1.4 Equation (1) of FKS2
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\(E_\pi (x) = |\pi (x) - \mathrm{Li}(x)| / \mathrm{Li}(x)\)

Definition 9.1.5 Equation (2) of FKS2
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\(E_\theta (x) = |\theta (x) - x| / x\)

Definition 9.1.6 Definition 1, FKS2
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We say that \(E_\theta \) satisfies a classical bound with parameters \(A, B, C, R, x_0\) if for all \(x \geq x_0\) we have

\[ E_\theta (x) \leq A \left(\frac{\log x}{R}\right)^B \exp \left(-C \left(\frac{\log x}{R}\right)^{1/2}\right). \]

Similarly for \(E_\pi \).

Definition 9.1.7 Definition 1, FKS2
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We say that \(E_\pi \) satisfies a classical bound with parameters \(A, B, C, R, x_0\) if for all \(x \geq x_0\) we have

\[ E_\pi (x) \leq A \left(\frac{\log x}{R}\right)^B \exp \left(-C \left(\frac{\log x}{R}\right)^{1/2}\right). \]

9.2 The prime number bounds of Rosser and Schoenfeld

In this section we formalize the prime number bounds of Rosser and Schoenfeld [ 11 ] .

Theorem 9.2.1 A medium version of the prime number theorem
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\(\vartheta (x) = x + O( x / \log ^2 x)\).

Proof

This in principle follows by establishing an analogue of Theorem 5.0.1, using mediumPNT in place of weakPNT.

Definition 9.2.1 Meissel-Mertens constant B
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\(B := \lim _{x \to \infty } \left( \sum _{p \leq x} \frac{1}{p} - \log \log x \right)\).

Definition 9.2.2 Mertens constant E
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\(E := \lim _{x \to \infty } \left( \sum _{p \leq x} \frac{\log p}{p} - \log x \right)\).

Sublemma 9.2.1 The Chebyshev function is Stieltjes
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The function \(\vartheta (x) = \sum _{p \leq x} \log p\) defines a Stieltjes function (monotone and right continuous).

Proof

Trivial

Sublemma 9.2.2 RS-prime display before (4.13)
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\(\sum _{p \leq x} f(p) = \int _{2}^x \frac{f(y)}{\log y}\ d\vartheta (y)\).

Proof

This follows from the definition of the Stieltjes integral.

Sublemma 9.2.3 RS equation (4.13)
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\(\sum _{p \leq x} f(p) = \frac{f(x) \vartheta (x)}{\log x} - \int _2^x \vartheta (x) \frac{d}{dy}( \frac{f(y)}{\log y} )\ dy.\)

Proof

Follows from Sublemma 9.2.2 and integration by parts.

Sublemma 9.2.4 RS equation (4.14)
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\[ \sum _{p \leq x} f(p) = \int _2^x \frac{f(y)\ dy}{\log y} + \frac{2 f(2)}{\log 2} \]
\[ + \frac{f(x) (\vartheta (x) - x)}{\log x} - \int _2^x (\vartheta (y) - y) \frac{d}{dy} \frac{d}{dy}( \frac{f(y)}{\log y} )\ dy. \]
Proof

Follows from Sublemma 9.2.3 and integration by parts.

Sublemma 9.2.5 RS equation (4.16)
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\[ L_f := \frac{2f(2)}{\log 2} - \int _2^\infty (\vartheta (y) - y) \frac{d}{dy} (\frac{f(y)}{\log y})\ dy. \]
Sublemma 9.2.6 RS equation (4.15)
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\[ \sum _{p \leq x} f(p) = \int _2^x \frac{f(y)\ dy}{\log y} + L_f \]
\[ + \frac{f(x) (\vartheta (x) - x)}{\log x} + \int _x^\infty (\vartheta (y) - y) \frac{d}{dy} \frac{d}{dy}( \frac{f(y)}{\log y} )\ dy. \]
Proof

Follows from Sublemma 9.2.4 and Definition 9.2.5.

Sublemma 9.2.7 RS equation (4.17)
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\[ \pi (x) = \frac{\vartheta (x)}{\log x} + \int _2^x \frac{\vartheta (y)\ dy}{y \log ^2 y}. \]
Proof

Follows from Sublemma 9.2.3 applied to \(f(t) = 1\).

Sublemma 9.2.8 RS equation (4.18)
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\[ \sum _{p \leq x} \frac{1}{p} = \frac{\vartheta (x)}{x \log x} + \int _2^x \frac{\vartheta (y) (1 + \log y)\ dy}{y^2 \log ^2 y}. \]
Proof

Follows from Sublemma 9.2.3 applied to \(f(t) = 1/t\).

Theorem 9.2.2 RS equation (4.19) and Mertens’ second theorem
\[ \sum _{p \leq x} \frac{1}{p} = \log \log x + B + \frac{\vartheta (x) - x}{x \log x} \]
\[ - \int _2^x \frac{(\vartheta (y)-y) (1 + \log y)\ dy}{y^2 \log ^2 y}. \]
Proof

Follows from Sublemma 9.2.3 applied to \(f(t) = 1/t\). One can also use this identity to demonstrate convergence of the limit defining \(B\).

Theorem 9.2.3 RS equation (4.19) and Mertens’ first theorem
\[ \sum _{p \leq x} \frac{\log p}{p} = \log x + E + \frac{\vartheta (x) - x}{x} \]
\[ - \int _2^x \frac{(\vartheta (y)-y)\ dy}{y^2}. \]
Proof

Follows from Sublemma 9.2.3 applied to \(f(t) = \log t / t\). Convergence will need Theorem 9.2.1.

9.3 Tools from BKLNW

In this file we record the results from [ 1 ] . -

9.4 The implications of FKS2

In this file we record the implications in the paper [ 7 ] that allow one to convert primary bounds on \(E_\psi \) into secondary bounds on \(E_\pi \), \(E_\theta \).

Remark 9.4.1 Remark in FKS2 Section 1.1
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\(\operatorname {li}(x) - \operatorname {Li}(x) = \operatorname {li}(2)\).

Proof

This follows directly from the definitions of \(\operatorname {li}\) and \(\operatorname {Li}\).

Definition 9.4.1 g function, FKS2 (16)
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For any \(a,b,c,x \in \mathbb {R}\) we define \(g(a,b,c,x) := x^{-a} (\log x)^b \exp ( c (\log x)^{1/2} )\).

Sublemma 9.4.1 FKS2 equation (17)

For any \(2 \leq x_0 {\lt} x\) one has

\[ (\pi (x) - \operatorname {Li}(x)) - (\pi (x_0) - \operatorname {Li}(x_0)) = \frac{\theta (x) - x}{\log x} - \frac{\theta (x_0) - x_0}{\log x_0} + \int _{x_0}^x \frac{\theta (t) - t}{t \log ^2 t} dt. \]
Proof

This follows from Sublemma 9.2.7.

Sublemma 9.4.2 FKS2 Sublemma 10-1
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We have

\[ \frac{d}{dx} g(a, b, c, x) = \left( -a \log (x) + b + \frac{c}{2}\sqrt{\log (x)} \right) x^{-a-1} (\log (x))^{b-1} \exp (c\sqrt{\log (x)}). \]
Proof

This follows from straightforward differentiation.

Sublemma 9.4.3 FKS2 Sublemma 10-2
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\(\frac{d}{dx} g(a, b, c, x) \) is negative when \(-au^2 + \frac{c}{2}u + b {\lt} 0\), where \(u = \sqrt{\log (x)}\).

Proof

Clear from previous sublemma.

Lemma 9.4.1 FKS2 Lemma 10a
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If \(a{\gt}0\), \(c{\gt}0\) and \(b {\lt} -c^2/16a\), then \(g(a,b,c,x)\) decreases with \(x\).

Proof

We apply Lemma 9.4.3. There are no roots when \(b {\lt} -\frac{c^2}{16a}\), and the derivative is always negative in this case.

Lemma 9.4.2 FKS2 Lemma 10b
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For any \(a{\gt}0\), \(c{\gt}0\) and \(b \geq -c^2/16a\), \(g(a,b,c,x)\) decreases with \(x\) for \(x {\gt} \exp ((\frac{c}{4a} + \frac{1}{2a} \sqrt{\frac{c^2}{4} + 4ab})^2)\).

Proof

We apply Lemma 9.4.3. If \(a {\gt} 0\), there are two real roots only if \(\frac{c^2}{4} + 4ab \geq 0\) or equivalently \(b \geq -\frac{c^2}{16a}\), and the derivative is negative for \(u {\gt} \frac{\frac{c}{2} + \sqrt{\frac{c^2}{4} + 4ab}}{2a}\).

Lemma 9.4.3 FKS2 Lemma 10c
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If \(c{\gt}0\), \(g(0,b,c,x)\) decreases with \(x\) for \(\sqrt{\log x} {\gt} -2b/c\).

Proof

We apply Lemma 9.4.3. If \(a = 0\), it is negative when \(u {\lt} \frac{-2b}{c}\).

Corollary 9.4.1 FKS2 Corollary 11
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If \(B \geq 1 + C^2 / 16R\) then \(g(1,1-B,C/\sqrt{R},x)\) is decreasing in \(x\).

Proof

This follows from Lemma 9.4.1 applied with \(a=1\), \(b=1-B\) and \(c=C/\sqrt{R}\).

Definition 9.4.2 Dawson function, FKS2 (19)
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The Dawson function \(D_+ : \mathbb {R} \to \mathbb {R}\) is defined by the formula \(D_+(x) := e^{-x^2} \int _0^x e^{t^2}\ dt\).

Remark 9.4.2 FKS2 remark after Corollary 11
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The Dawson function has a single maximum at \(x \approx 0.942\), after which the function is decreasing.

Proof

The Dawson function satisfies the differential equation \(F'(x) + 2xF(x) = 1\) from which it follows that the second derivative satisfies \(F''(x) = −2F(x) − 2x(−2xF(x) + 1)\), so that at every critical point (where we have \(F(x) = \frac{1}{2x}\)) we have \(F''(x) = −\frac{1}{x}\). It follows that every positive critical value gives a local maximum, hence there is a unique such critical value and the function decreases after it. Numerically one may verify this is near 0.9241 see https://oeis.org/ A133841.

Lemma 9.4.4 FKS2 Lemma 12

Suppose that \(E_\theta \) satisfies an admissible classical bound with parameters \(A,B,C,R,x_0\). Then, for all \(x \geq x_0\),

\[ \int _{x_0}^x |\frac{E_\theta (t)}{\log ^2 t} dt| \leq \frac{2A}{R^B} x m(x_0,x) \exp (-C \sqrt{\frac{\log x}{R}}) D_+( \sqrt{\log x} - \frac{C}{2\sqrt{R}} ) \]

where

\[ m(x_0,x) = \max ( (\log x_0)^{(2B-3)/2}, (\log x)^{(2B-3)/2} ). \]
Proof

Since \(\varepsilon _{\theta ,\mathrm{asymp}}(t)\) provides an admissible bound on \(\theta (t)\) for all \(t \geq x_0\), we have

\[ \int _{x_0}^{x} \left| \frac{\theta (t) - t}{t(\log (t))^2} \right| dt \leq \int _{x_0}^{x} \frac{\varepsilon _{\theta ,\mathrm{asymp}}(t)}{(\log (t))^2} = \frac{A_\theta }{R^B} \int _{x_0}^{x} (\log (t))^{B-2} \exp \left( -C\sqrt{\frac{\log (t)}{R}} \right) dt. \]

We perform the substitution \(u = \sqrt{\log (t)}\) and note that \(u^{2B-3} \leq m(x_0, x)\) as defined in (21). Thus the above is bounded above by

\[ \frac{2A_\theta m(x_0, x)}{R^B} \int _{\sqrt{\log (x_0)}}^{\sqrt{\log (x)}} \exp \left( u^2 - \frac{Cu}{\sqrt{R}} \right) du. \]

Then, by completing the square \(u^2 - \frac{Cu}{\sqrt{R}} = \left( u - \frac{C}{2\sqrt{R}} \right)^2 - \frac{C^2}{4R}\) and doing the substitution \(v = u - \frac{C}{2\sqrt{R}}\), the above becomes

\[ \frac{2A_\theta m(x_0, x)}{R^B} \exp \left( -\frac{C^2}{4R} \right) \int _{\sqrt{\log (x_0)} - \frac{C}{2\sqrt{R}}}^{\sqrt{\log (x)} - \frac{C}{2\sqrt{R}}} \exp (v^2) \, dv. \]

Now we have

\begin{align*} \int _{\sqrt{\log (x_0)} - \frac{C}{2\sqrt{R}}}^{\sqrt{\log (x)} - \frac{C}{2\sqrt{R}}} \exp (v^2) \, dv & \leq \int _{0}^{\sqrt{\log (x)} - \frac{C}{2\sqrt{R}}} \exp (v^2) \, dv \\ & = x \exp \left( \frac{C^2}{4R} \right) \exp \left( -C\sqrt{\frac{\log (x)}{R}} \right) D_+\left( \sqrt{\log (x)} - \frac{C}{2\sqrt{R}} \right). \end{align*}

Combining the above completes the proof.

Theorem 9.4.1 FKS2 Proposition 13
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Suppose that \(A_\psi ,B,C,R,x_0\) give an admissible bound for \(E_\psi \). If \(B {\gt} C^2/8R\), then \(A_\theta , B, C, R, x_0\) give an admissible bound for \(E_\theta \), where

\[ A_\theta = A_\psi (1 + \nu _{asymp}(x_0)) \]

with

\[ \nu _{asymp}(x_0) = \frac{1}{A_\psi } (\frac{R}{\log x_0})^B \exp (C \sqrt{\frac{\log x_0}{R}}) (a_1 (\log x_0) x_0^{-1/2} + a_2 (\log x_0) x_0^{-2/3}). \]
Proof
Theorem 9.4.2 FKS2 Corollary 14
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We have an admissible bound for \(E_\theta \) with \(A = 121.0961\), \(B=3/2\), \(C=2\), \(R = 5.5666305\), \(x_0=2\).

Proof
Definition 9.4.3 mu asymptotic function, FKS2 (9)

For \(x_0,x_1 {\gt} 0\), we define

\[ mu_{asymp}(x_0,x_1) := \frac{x_0 \log (x_1)}{\epsilon _{\theta ,asymp}(x_1) x_1 \log (x_0)} \left|\frac{\pi (x_0) - \operatorname {Li}(x_0)}{x_0/\log x_0} - \frac{\theta (x_0) - x_0}{x_0}\right| + \frac{2D_+(\sqrt{\log (x_1)} - \frac{C}{2\sqrt{R}}}{\sqrt{\log x_1}} \]

.

Definition 9.4.4 FKS2 Definition 5
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Let \(x_0 {\gt} 2\). We say a (step) function \(ε_{\diamond ,num}(x_0)\) gives an admissible numerical bound for \(E_\diamond (x)\) if \(E_\diamond (x) \leq ε_{\diamond ,num}(x_0)\) for all \(x \geq x_0\).

Theorem 9.4.3 FKS2 Remark 7

If

\[ \frac{d}{dx} \frac{\log x}{x} \left( Li(x) - \frac{x}{\log x} - Li(x_1) + \frac{x_1}{\log x_1} \right)|_{x_2} \geq 0 \]

then \(\mu _{num,1}(x_0,x_1,x_2) {\lt} \mu _{num,2}(x_0,x_1)\).

Proof
Theorem 9.4.4 FKS2 Remark 15
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If \(\log x_0 \geq 1000\) then we have an admissible bound for \(E_\theta \) with the indicated choice of \(A(x_0)\), \(B = 3/2\), \(C = 2\), and \(R = 5.5666305\).

Proof
Theorem 9.4.5 FKS2 Theorem 3

If \(B \geq \max (3/2, 1 + C^2/16 R)\), \(x_0 {\gt} 0\), and one has an admissible asymptotic bound with parameters \(A,B,C,x_0\) for \(E_\theta \), and

\[ x_1 \geq \max ( x_0, \exp ( (1 + \frac{C}{2\sqrt{R}}))^2), \]

then

\[ E_\pi (x) \leq \epsilon _{\theta ,asymp}(x_1) ( 1 + \mu _{asymp}(x_0,x_1) ) \]

for all \(x \geq x_1\). In other words, we have an admissible bound with parameters \((1+\mu _{asymp}(x_0,x_1))A, B, C, x_1\) for \(E_\pi \).

Proof
Theorem 9.4.6 FKS2 Proposition 17
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Let \(x {\gt} x_0 {\gt} 2\). IF \(E_\psi (x) \leq \varepsilon _{\psi ,num}(x_0)\), then

\[ - \varepsilon _{\theta ,num}(x_0) \leq \frac{\theta (x)-x}{x} \leq \varepsilon _{\psi ,num}(x_0) \leq \varepsilon _{\theta ,num}(x_0) \]

where

\[ \varepsilon _{\theta ,num}(x_0) = \varepsilon _{\psi ,num}(x_0) + 1.00000002(x_0^{-1/2}+x_0^{-2/3}+x_0^{-4/5}) + 0.94 (x_0^{-3/4} + x_0^{-5/6} + x_0^{-9/10}) \]
Proof
Theorem 9.4.7 FKS2 Lemma 19

Let \(x_1 {\gt} x_0 \geq 2\), \(N \in \mathbb {N}\), and let \((b_i)_{i=1}^N\) be a finite partition of \([x_0,x_1]\). Then

\[ |\int _{x_0}^{x_1} \frac{\theta (t)-t}{t \log ^2 t}\ dt| \leq \sum _{i=1}^{N-1} \varepsilon _{\theta ,num}(e^{b_i}) (Li(e^{b_{i+1}}) - Li(e^{b_i}) + \frac{e^{b_i}}{b_i} - \frac{e^{b_{i+1}}}{b_{i+1}}). \]
Proof
Theorem 9.4.8 FKS2 Lemma 20

Assume \(x \geq 6.58\). Then \(Li(x) - \frac{x}{\log x}\) is strictly increasing and \(Li(x) - \frac{x}{\log x} {\gt} \frac{x-6.58}{\log ^2 x} {\gt} 0\).

Proof

Let \(x_0 {\gt} 0\) 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

\[ \mu _{num}(x_0,x_1,x_2) = \frac{x_0 \log x_1}{\varepsilon _{\theta ,num}(x_0) x_1 \log x_0} \left|\frac{\pi (x_0) - \operatorname {Li}(x_0)}{x_0/\log x_0} - \frac{\theta (x_0) - x_0}{x_0}\right| \]
\[ + \frac{\log x_1}{\varepsilon _{theta,num}(x_1) x_1} \sum _{i=1}^{N-1} \varepsilon _{\theta ,num}(\exp (b_i)) \left( Li(e^{b_{i+1}}) - Li(e^{b_i}) + \frac{e^{b_i}}{b_i} - \frac{e^{b_{i+1}}}{b_{i+1}}\right) \]
\[ + \frac{\log x_2}{x_2} \left( Li(x_2) - \frac{x_2}{\log x_2} - Li(x_1) + \frac{x_1}{\log x_1} \right) \]

and for \(x_2 {\gt} x_1 \log x_1\), including the case \(x_2 = \infty \), we define

\[ \mu _{num}(x_0,x_1,x_2) = \frac{x_0 \log x_1}{\varepsilon _{\theta ,num}(x_1) x_1 \log x_0} \left|\frac{\pi (x_0) - \operatorname {Li}(x_0)}{x_0/\log x_0} - \frac{\theta (x_0) - x_0}{x_0}\right| \]
\[ + \frac{\log x_1}{\varepsilon _{\theta ,num}(x_1) x_1} \sum _{i=1}^{N-1} \varepsilon _{\theta ,num}(\exp (b_i)) \left( Li(e^{b_{i+1}}) - Li(e^{b_i}) + \frac{e^{b_i}}{b_i} - \frac{e^{b_{i+1}}}{b_{i+1}}\right) \]
\[ + \frac{1}{\log x_1 + \log \log x_1 - 1}. \]

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
Theorem 9.4.10 FKS2 Corollary 8

Let \(\{ b'_i\} _{i=1}^M\) be a set of finite subdivisions of \([\log (x_1),\infty )\), with \(b'_1 = \log (x_1)\) and \(b'_M = \infty \). Define

\[ \varepsilon _{\pi , num}(x_1) := \max _{1 \leq i \leq M-1}\varepsilon _{\pi , num}(\exp (b'_i), \exp (b'_{i+1})). \]

Then \(E_\pi (x) \leq \varepsilon _{\pi ,num}(x_1)\) for all \(x \geq x_1\).

Proof
Theorem 9.4.11 FKS2 Corollary 21

Let \(B \geq \max (\frac{3}{2}, 1 + \frac{C^2}{16R})\). Let \(x_0, x_1 {\gt} 0\) with \(x_1 \geq \max (x_0, \exp ( (1 + \frac{C}{2\sqrt{R}})^2))\). If \(E_\psi \) satisfies an admissible classical bound with parameters \(A_\psi ,B,C,R,x_0\), then \(E_\pi \) satisfies an admissible classical bound with \(A_\pi , B, C, R, x_1\), where

\[ A_\pi = (1 + \nu _{asymp}(x_0)) (1 + \mu _{asymp}(x_0, x_1)) A_\psi \]

for all \(x \geq x_0\), where

\[ |E_\theta (x)| \leq \varepsilon _{\theta ,asymp}(x) := A (1 + \mu _{asymp}(x_0,x)) \exp (-C \sqrt{\frac{\log x}{R}}) \]

where

\[ \nu _{asymp}(x_0) = \frac{1}{A_\psi } (\frac{R}{\log x_0})^B \exp (C \sqrt{\frac{\log x_0}{R}}) (a_1 (\log x_0) x_0^{-1/2} + a_2 (\log x_0) x_0^{-2/3}) \]

and

\[ \mu _{asymp}(x_0,x_1) = \frac{x_0 \log x_1}{\varepsilon _{\theta ,asymp}(x_1)x_1 \log x_0} |E_\pi (x_0) - E_\theta (x_0)| + \frac{2 D_+(\sqrt{\log x} - \frac{C}{2\sqrt{R}})}{\sqrt{\log x_1}}. \]
Proof
Theorem 9.4.12 FKS2 Corollary 22
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One has

\[ |\pi (x) - \mathrm{Li}(x)| \leq 9.2211 x \sqrt{\log x} \exp (-0.8476 \sqrt{\log x}) \]

for all \(x \geq 2\).

Proof
Theorem 9.4.13 FKS2 Corollary 23
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\(A_\pi , B, C, x_0\) as in Table 6 give an admissible asymptotic bound for \(E_\pi \) with \(R = 5.5666305\).

Proof
Theorem 9.4.14 FKS2 Corollary 24
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We have the bounds \(E_\pi (x) \leq B(x)\), where \(B(x)\) is given by Table 7.

Proof
Theorem 9.4.15 FKS2 Corollary 26
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One has

\[ |\pi (x) - \mathrm{Li}(x)| \leq 0.4298 \frac{x}{\log x} \]

for all \(x \geq 2\).

Proof

9.5 Summary of results

Here we list some papers that we plan to incorporate into this section in the future, and list some results that have not yet been moved into dedicated paper sections.

References to add:

Dusart: https://piyanit.nl/wp-content/uploads/2020/10/art_10.1007_s11139-016-9839-4.pdf

PT: D. J. Platt and T. S. Trudgian, The error term in the prime number theorem, Math. Comp. 90 (2021), no. 328, 871–881.

JY: D. R. Johnston, A. Yang, Some explicit estimates for the error term in the prime number theorem, arXiv:2204.01980.

Theorem 9.5.1 PT Corollary 2
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One has

\[ |\pi (x) - \mathrm{Li}(x)| \leq 235 x (\log x)^{0.52} \exp (-0.8 \sqrt{\log x}) \]

for all \(x \geq \exp (2000)\).

Proof
Theorem 9.5.2 JY Corollary 1.3
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One has

\[ |\pi (x) - \mathrm{Li}(x)| \leq 9.59 x (\log x)^{0.515} \exp (-0.8274 \sqrt{\log x}) \]

for all \(x \geq 2\).

Proof
Theorem 9.5.3 JY Theorem 1.4
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One has

\[ |\pi (x) - \mathrm{Li}(x)| \leq 0.028 x (\log x)^{0.801} \exp (-0.1853 \log ^{3/5} x / (\log \log x)^{1/5})) \]

for all \(x \geq 2\).

Proof

TODO: input other results from JY

Theorem 9.5.4 Dusart Proposition 5.4
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There exists a constant \(X_0\) (one may take \(X_0 = 89693\)) with the following property: for every real \(x \geq X_0\), there exists a prime \(p\) with

\[ x {\lt} p \le x\Bigl(1 + \frac{1}{\log ^3 x}\Bigr). \]
Proof

TODO: input other results from Dusart