Wiki
Wiki

Research notes on every problem, and a library of the papers behind them. Built from the open erdos repository.

Updated


Source. Liu--Sawhney, On further questions regarding unit fractions, arXiv:2404.07113v1, Lemma 3.1, p. 9; see the source digest.

Statement. Let NN be sufficiently large, N0.95≤M≤NN^{0.95}\leq M\leq N, and A⊆[M,N]∩ZA\subseteq[M,N]\cap\mathbb Z with ∣A∣≥N0.95|A|\geq N^{0.95}. For each n∈An\in A, let

(log⁡N)−2≤pn≤1−(log⁡N)−2.(\log N)^{-2}\leq p_n\leq1-(\log N)^{-2}.

Let Q>0Q>0 and choose xx so that x/Q=∑n∈Apn/nx/Q=\sum_{n\in A}p_n/n. With e(t)=exp⁡(2πit)e(t)=\exp(2\pi it) and the sum over integer frequencies hh,

1Q∑∣h∣≤M/2Re⁡ ⁣(e(−hx/Q)∏n∈A(1−pn+pne(h/n)))≥34Q.\frac1Q\sum_{|h|\leq M/2} \operatorname{Re}\!\left( e(-hx/Q)\prod_{n\in A}(1-p_n+p_ne(h/n))\right) \geq\frac3{4Q}.

In the later applications QQ is a positive integer common denominator and xx is an integer. This lemma's proof requires only Q>0Q>0 and the displayed relation between x/Qx/Q and the probabilities.

Proof. Write

F(h)=e(−hx/Q)∏n∈A(1−pn+pne(h/n)),H=M3/5.F(h)=e(-hx/Q)\prod_{n\in A}(1-p_n+p_ne(h/n)), \qquad H=M^{3/5}.

First consider ∣h∣≤H|h|\leq H. The Taylor estimate in Fact 2.5 gives, uniformly in n∈An\in A,

1−pn+pne(h/n)=e(pnh/n)exp⁡ ⁣(−2π2pn(1−pn)h2n2)(1+O ⁣(∣h∣3n3)).1-p_n+p_ne(h/n) =e(p_nh/n) \exp\!\left(-\frac{2\pi^2p_n(1-p_n)h^2}{n^2}\right) \left(1+O\!\left(\frac{|h|^3}{n^3}\right)\right).

The conversion of the quadratic polynomial to an exponential introduces an error O(h4/n4)O(h^4/n^4), which is absorbed by O(∣h∣3/n3)O(|h|^3/n^3) because ∣h∣/n≤M−2/5|h|/n\leq M^{-2/5}. The phase factors cancel exactly:

e(−hx/Q)∏n∈Ae(pnh/n)=1.e(-hx/Q)\prod_{n\in A}e(p_nh/n)=1.

Moreover,

∑n∈A∣h∣3n3≤H3∑n≥⌈M⌉1n3≪M9/5M−2=M−1/5.\sum_{n\in A}\frac{|h|^3}{n^3} \leq H^3\sum_{n\geq\lceil M\rceil}\frac1{n^3} \ll M^{9/5}M^{-2}=M^{-1/5}.

Multiplying the error factors therefore yields

F(h)=(1+O(M−1/5))exp⁡ ⁣(−∑n∈A2π2pn(1−pn)h2n2),F(h)=\bigl(1+O(M^{-1/5})\bigr) \exp\!\left(-\sum_{n\in A} \frac{2\pi^2p_n(1-p_n)h^2}{n^2}\right),

where the error may be complex. For large NN its real part is at least −1/6-1/6, and the exponential is positive. Hence

1Q∑∣h∣≤HRe⁡F(h)≥56Q∑∣h∣≤Hexp⁡ ⁣(−∑n∈A2π2pn(1−pn)h2n2)≥56Q,\frac1Q\sum_{|h|\leq H}\operatorname{Re}F(h) \geq\frac5{6Q}\sum_{|h|\leq H} \exp\!\left(-\sum_{n\in A} \frac{2\pi^2p_n(1-p_n)h^2}{n^2}\right) \geq\frac5{6Q},

using just the term h=0h=0 for the last inequality.

For the remaining frequencies H<∣h∣≤M/2H<|h|\leq M/2, we have ∣h∣/n≤1/2|h|/n\leq1/2. The absolute-value bound in Fact 2.5 gives

∣F(h)∣≤∏n∈A(1−8pn(1−pn)h2n2)≤exp⁡ ⁣(−8h2∑n∈Apn(1−pn)n2).|F(h)|\leq\prod_{n\in A} \left(1-\frac{8p_n(1-p_n)h^2}{n^2}\right) \leq\exp\!\left(-8h^2\sum_{n\in A} \frac{p_n(1-p_n)}{n^2}\right).

Put δ=(log⁡N)−2\delta=(\log N)^{-2}. For large NN, pn(1−pn)≥δ(1−δ)≥δ/2p_n(1-p_n)\geq\delta(1-\delta)\geq\delta/2. Since n≤Nn\leq N, ∣A∣≥N0.95|A|\geq N^{0.95}, and M≥N0.95M\geq N^{0.95},

∣F(h)∣≤exp⁡ ⁣(−4δ∣A∣M6/5N2)≤exp⁡ ⁣(−4N0.09(log⁡N)2).|F(h)| \leq\exp\!\left(-\frac{4\delta |A|M^{6/5}}{N^2}\right) \leq\exp\!\left(-\frac{4N^{0.09}}{(\log N)^2}\right).

There are at most M+1≤N+1M+1\leq N+1 integer frequencies in this range. Their total absolute contribution is consequently o(1/Q)o(1/Q), and for large NN is at most 1/(12Q)1/(12Q). Combining the two ranges gives 5/(6Q)−1/(12Q)=3/(4Q)5/(6Q)-1/(12Q)=3/(4Q), as claimed.

Source formula corrections. The source propagates Fact 2.5's 2π2\pi coefficient typo; the proof above uses 2π22\pi^2, as required by the displayed Taylor expansion. Its first proof display also uses exp⁡(pnh/n)\exp(p_nh/n) where the cancellation requires the phase e(pnh/n)e(p_nh/n). The final tail estimate here retains the factor 1/Q1/Q, making its comparison with the major-arc lower bound explicit. These are local formula clarifications; the argument and constants in the conclusion are those of the source.

Dependencies. Fact 2.5, the summability of n−3n^{-3}, and elementary exponential estimates.

Bears on. #298 and #299, through the major-arc part of the quantitative reciprocal-sum criterion.