Unit Fractions

2 Basic Estimates

This section contains standard estimates from analytic number theory that will be required.

Lemma 2.1

For any \(X\geq 3\)

\[ \sum _{n\leq X}\omega (n) = X\log \log X+O(X). \]

Proof

Since \(\omega (n) = \sum _{p\leq n}1_{p\mid n}\), the left-hand side equals, after a change in the order of summation,

\[ \sum _{p\leq X}\sum _{n\leq X}1_{p\mid n} = \sum _{p\leq X}\left\lfloor \frac{X}{p}\right\rfloor . \]

Since \(\lfloor x\rfloor =x+O(1)\), this is equal to

\[ X\sum _{p\leq X}\frac{1}{p} +O(\pi (X)) = X\log \log X+O(X), \]

using Lemma 2.7 and the trivial estimate \(\pi (X)\ll X\).

Lemma 2.2

For any \(X\geq 3\)

\[ \sum _{n\leq X}\omega (n)^2 \leq X(\log \log X)^2+O(X\log \log X). \]

Proof

Since \(\omega (n) = \sum _{p\leq n}1_{p\mid n}\), the left-hand side is equal to, after expanding the sum and rearranging,

\[ \sum _{p,q\leq X}\sum _{n\leq X}1_{p\mid n}1_{q\mid n}. \]

The part of the sum where \(p=q\) is

\[ \sum _{p\leq X}\sum _{n\leq X}\lfloor X/p\rfloor = X\log \log X+O(X), \]

as in the proof of Lemma 2.1. If \(p\neq q\), then \(p\mid n\) and \(q\mid n\) if and only if \(pq\mid n\), and so the sum over \(n\) is bounded above by

\[ \sum _{n\leq X}1_{pq\mid n}= \lfloor X/pq\rfloor \leq X/pq. \]

Therefore

\[ \sum _{\substack {p,q\leq X\\ p\neq q}}\sum _{n\leq X}1_{p\mid n}1_{q\mid n}\leq X\sum _{\substack {p,q\leq X\\ p\neq q}}\frac{1}{pq}\leq X\sum _{p,q\leq X}\frac{1}{pq}\leq X\left( \sum _{p\leq X}\frac{1}{p}\right)^2. \]

By Lemma 2.7 this is \(X(\log \log X+O(1))^2 = X(\log \log X)^2+O(X\log \log X)\). The lemma follows by combining the estimates on the two parts of the sum.

Lemma 2.3 Turán’s estimate
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For any \(X\geq 3\)

\[ \sum _{n\leq X}(\omega (n) - \log \log X)^2 \ll X\log \log X. \]

Proof

The left-hand side equals

\[ \sum _{n\leq X}\omega (n)^2 - 2\log \log X\sum _{n\leq X}\omega (n)+\lfloor X\rfloor (\log \log X)^2. \]

The first summand is at most, by Lemma 2.2,

\[ X(\log \log X)^2 +O(X\log \log X). \]

The second summand is equal to, by Lemma 2.1,

\[ -2\log \log X(X\log \log X+O(X)) = -2X(\log \log X)^2 +O(X\log \log X). \]

The third summand is equal to

\[ (X+O(1))(\log \log X)^2 = X(\log \log X)^2 + O(X\log \log X). \]

Therefore the main terms cancel, and

\[ \sum _{n\leq X}(\omega (n) - \log \log X)^2\leq O(X\log \log X) \]

as required.

Lemma 2.4 Chebyshevs’ estimate
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For any \(X\geq 3\)

\[ \pi (X) \ll \frac{X}{\log X}. \]

Proof

Lemma 2.5 Divisor bound
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For any \(\epsilon \) such that \(0{\lt}\epsilon \leq 1\), if \(n\) is sufficiently large depending on \(\epsilon \), then

\[ \tau (n) \leq n^{(1+\epsilon )\frac{\log 2}{\log \log n}}. \]

Proof

We first show that, for any real \(K\geq 2\),

\[ \tau (n) \leq n^{1/K}K^{2^K}. \]

Write \(n\) as the product of unique prime powers \(n=p_1^{k_1}\cdots p_r^{k_r}\), so that

\[ \frac{\tau (n)}{n^{1/K}} = \frac{\prod _{i=1}^r (k_i+1)}{\prod _{i=1}^r p_i^{k_i/K}} = \prod _{i=1}^r \frac{k_i+1}{p_i^{k_i/K}}. \]

If \(p_i{\gt}2^K\) then

\[ \frac{k_i+1}{p_i^{k_i/K}}\leq \frac{k_i+1}{2^{k_i}}\leq 1, \]

since \(1+k \leq 2^k\) for all integer \(k\geq 0\) by Bernoulli’s inequality. Therefore

\[ \prod _{i=1}^r \frac{k_i+1}{p_i^{k_i/K}} \leq \prod _{\substack {1\leq i\leq r\\ p_i{\lt}2^K}}\frac{k_i+1}{p_i^{k_i/K}}. \]

If \(p_i{\lt}2^K\) then, since \(p_i\geq 2\),

\[ \frac{k_i+1}{p^{k_i/K}}\leq \frac{k_i+1}{2^{k_i/K}}\leq \frac{k_i+1}{k_i/K+1/2}, \]

using the fact that \(x+1/2\leq 2^x\) for all \(x\geq 0\). Since \(K\geq 2\) the denominator here is \(\geq (1+k_i)/K\), and so \(\frac{k_i+1}{p^{k_i/K}}\leq K\). Therefore

\[ \frac{\tau (n)}{n^{1/K}}\leq \prod _{\substack {1\leq i\leq r\\ p_i{\lt}2^K}} K\leq K^{\pi (2^K)}\leq K^{2^K}, \]

as required.

The second part of the proof is to apply the first part with

\[ K=\left( 1+\epsilon /2\right)^{-1}\frac{\log \log n}{\log 2}. \]

(The right-hand side tends to \(\infty \) as \(n\to \infty \), so for sufficiently large \(n\) we have \(K\geq 2\) as required.)

Note that \(2^K= (\log n)^{\frac{2}{2+\epsilon }}\leq (\log n)^{1-\epsilon }\) (since \(1-\epsilon {\gt}\frac{2}{2+\epsilon }\), since \(\epsilon \leq 1\)) and

\[ \log K \leq \log \log \log n. \]

Taking logarithms of the inequality in the first part,

\[ \log \tau (n) \leq \frac{\log n}{K}+2^K\log K\leq \log n\left( \frac{1}{K}+\frac{\log \log \log n}{(\log n)^\epsilon }\right) \]
\[ = \log n\frac{\log 2}{\log \log n}\left( 1+\epsilon /2+\frac{(\log \log \log n)(\log \log n)}{(\log 2)(\log n)^{\epsilon }}\right). \]

For any fixed \(\epsilon {\gt}0\), the function \(\frac{(\log \log \log n)(\log \log n)}{(\log 2)(\log n)^{\epsilon }}\to 0\) as \(n\to \infty \), so for sufficiently large \(n\) it is \(\leq \epsilon /2\), and hence

\[ \log \tau (n) \leq \log n\frac{\log 2}{\log \log n}(1+\epsilon ) \]

as required.

Lemma 2.6 Mertens’ estimate
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There exists a constant \(c\) such that

\[ \sum _{q\leq X}\frac{1}{q} = \log \log X+c+O(1/\log X), \]

where the sum is restricted to prime powers.

Proof

Lemma 2.7 Mertens’ estimate, just for primes
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There exists a constant \(c\) such that

\[ \sum _{p\leq X}\frac{1}{p} = \log \log X+c+O(1/\log X), \]

where the sum is restricted to primes.

Proof

Lemma 2.8 Mertens’ product estimate
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For any \(X\geq 2\),

\[ \prod _{p\leq X}\left( 1-\frac{1}{p}\right)^{-1}\asymp \log X. \]

Proof

Lemma 2.9 Sieve of Eratosthenes-Legendre
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For any \(x,y\geq 0\) and \(u\geq v\geq 1\)

\[ \# \{ n\in (x,x+y] : p\mid n\implies p\not\in [u,v]\} = y\prod _{u\leq p\leq v}\left( 1-\frac{1}{p}\right)+O(2^v). \]

Proof

Let \(P = \prod _{u\leq p\leq v}p\). The left-hand side of the estimate in the lemma can be written as

\[ \sum _{x\leq n{\lt}x+y}1_{(n,P)=1}. \]

Using the identity \(\sum _{d\mid m}\mu (d) = 1\) if \(m=1\) and \(0\) otherwise, the fact that \(d\mid (n,P)\) if and only if \(d\mid n\) and \(d\mid P\), and that \(\sum _{n\leq z}1_{d\mid n}=\left\lfloor \frac{z}{d}\right\rfloor \) for any \(z\geq 0\),

\[ \sum _{x{\lt} n\leq x+y}1_{(n,P)=1}= \sum _{x{\lt} n\leq x+y}\sum _{d\mid (n,P)}\mu (d) \]
\[ = \sum _{x{\lt} n\leq x+y}\sum _{\substack {d\mid n\\ d\mid P}}\mu (d) \]
\[ =\sum _{d\mid P}\mu (d)\sum _{x{\lt} n\leq x+y}1_{d\mid n} \]
\[ =\sum _{d\mid P}\mu (d)\left(\left\lfloor \frac{x+y}{d}\right\rfloor -\left\lfloor \frac{x}{d}\right\rfloor \right). \]

Since \(\lfloor X\rfloor = X+O(1)\) for any \(X\), we have

\[ \left\lfloor \frac{x+y}{d}\right\rfloor -\left\lfloor \frac{x}{d}\right\rfloor =\frac{y}{d}+O(1) \]

for any \(x,y,d\), and hence

\[ \sum _{x{\lt} n\leq x+y}1_{(n,P)=1}=y\sum _{d\mid P}\frac{\mu (d)}{d}+O(\sum _{d\mid P}1). \]

We have \(\sum _{d\mid P}1=\tau (P)=2^k\) where \(k\) is the number of primes in \([u,v]\), which is at most \(v\), so the error term here is \(O(2^v)\). Finally, expanding out the product shows that

\[ \sum _{d\mid P}\frac{\mu (d)}{d} = \prod _{p\in [u,v]}\left( 1-\frac{1}{p}\right). \]

Inserting this into the above finishes the proof.