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IMC / 2020 / Problems / Day 2, P8

IMC 2020 · Day 2 · P8

killer

Compute lim⁡n→∞1log⁡log⁡n∑k=1n(−1)k(nk)log⁡k.\lim_{n \to \infty} \frac{1}{\log \log n} \sum_{k=1}^{n} (-1)^k \binom{n}{k} \log k. (Here log⁡\log denotes the natural logarithm.)

Fedor Petrov, St. Petersburg State University

Solution 1 of 2 (official)

Answer: 1.

The idea is that if f(k)=∫gkf(k) = \int g^k, then ∑(−1)k(nk)f(k)=∫(1−g)n.\sum (-1)^k \binom{n}{k} f(k) = \int (1 - g)^n. To relate this to logarithm, we may use the Frullani integrals ∫0∞e−x−e−kxx dx=lim⁡c→+0∫c∞e−xx dx−∫c∞e−kxx dx=lim⁡c→+0∫c∞e−xx dx−∫kc∞e−xx dx=lim⁡c→+0∫ckce−xx dx=log⁡k+lim⁡c→+0∫ckce−x−1x dx=log⁡k.\int_0^\infty \frac{e^{-x} - e^{-kx}}{x}\,dx = \lim_{c \to +0} \int_c^\infty \frac{e^{-x}}{x}\,dx - \int_c^\infty \frac{e^{-kx}}{x}\,dx = \lim_{c \to +0} \int_c^\infty \frac{e^{-x}}{x}\,dx - \int_{kc}^\infty \frac{e^{-x}}{x}\,dx = \lim_{c \to +0} \int_c^{kc} \frac{e^{-x}}{x}\,dx = \log k + \lim_{c \to +0} \int_c^{kc} \frac{e^{-x} - 1}{x}\,dx = \log k. This gives the integral representation of our sum: A:=∑k=1n(−1)k(nk)log⁡k=∫0∞−e−x+1−(1−e−x)nx dx.A := \sum_{k=1}^{n} (-1)^k \binom{n}{k} \log k = \int_0^\infty \frac{-e^{-x} + 1 - (1 - e^{-x})^n}{x}\,dx. Now the problem is reduced to a rather standard integral asymptotics.

We have (1−e−x)n⩾1−ne−x(1 - e^{-x})^n \geqslant 1 - n e^{-x} by Bernoulli inequality, thus 0⩽−e−x+1−(1−e−x)n⩽ne−x0 \leqslant -e^{-x} + 1 - (1 - e^{-x})^n \leqslant n e^{-x}, and we get 0⩽∫M∞−e−x+1−(1−e−x)nx dx⩽n∫M∞e−xx dx⩽nM−1∫M∞e−x dx=nM−1e−M.0 \leqslant \int_M^\infty \frac{-e^{-x} + 1 - (1 - e^{-x})^n}{x}\,dx \leqslant n \int_M^\infty \frac{e^{-x}}{x}\,dx \leqslant n M^{-1} \int_M^\infty e^{-x}\,dx = n M^{-1} e^{-M}. So choosing MM such that MeM=nM e^M = n (such MM exists and goes to ∞\infty with nn) we get A=O(1)+∫0M−e−x+1−(1−e−x)nx dx.A = O(1) + \int_0^M \frac{-e^{-x} + 1 - (1 - e^{-x})^n}{x}\,dx. Note that for 0⩽x⩽M0 \leqslant x \leqslant M we have e−x⩾e−M=M/ne^{-x} \geqslant e^{-M} = M / n, and (1−e−x)n−1⩽e−e−x(n−1)⩽e−M(n−1)/n(1 - e^{-x})^{n-1} \leqslant e^{-e^{-x} (n-1)} \leqslant e^{-M (n-1)/n} tends to 0 uniformly in xx. Therefore ∫0M(1−e−x)(1−(1−e−x)n−1)x dx=(1+o(1))∫0M1−e−xx dx.\int_0^M \frac{(1 - e^{-x})(1 - (1 - e^{-x})^{n-1})}{x}\,dx = (1 + o(1)) \int_0^M \frac{1 - e^{-x}}{x}\,dx. Finally ∫0M1−e−xx dx=∫011−e−xx dx+∫1M−e−xx dx+∫1Mdxx=log⁡M+O(1)=log⁡(M+log⁡M)+O(1)=log⁡log⁡n+O(1),\int_0^M \frac{1 - e^{-x}}{x}\,dx = \int_0^1 \frac{1 - e^{-x}}{x}\,dx + \int_1^M \frac{-e^{-x}}{x}\,dx + \int_1^M \frac{dx}{x} = \log M + O(1) = \log(M + \log M) + O(1) = \log \log n + O(1), and we get A=(1+o(1))log⁡log⁡nA = (1 + o(1)) \log \log n.

Solution 2 of 2 (official)

We start with a known identity (a finite difference of 1/x1/x).

Expand the rational function f(x)=m!x(x+1)…(x+m)f(x) = \frac{m!}{x (x + 1) \dots (x + m)} as the linear combination of simple fractions f(x)=∑j=0mcj/(x+j)f(x) = \sum_{j=0}^{m} c_j / (x + j). To find cjc_j we use cj=((x+j)f(x))∣x=−j=(−1)j(mj).c_j = ((x + j) f(x)) \big|_{x = -j} = (-1)^j \binom{m}{j}. So we get ∑k=0m(−1)k(mk)1x+k=m!x(x+1)…(x+m).(1)\tag{1} \sum_{k=0}^{m} (-1)^k \binom{m}{k} \frac{1}{x + k} = \frac{m!}{x (x + 1) \dots (x + m)}. Another known identity we use is ∑k=j+1n(−1)k(nj)=∑k=j+1n(−1)k((n−1k)+(n−1k−1))=(−1)j+1(n−1j).(2)\tag{2} \sum_{k=j+1}^{n} (-1)^k \binom{n}{j}

= \sum_{k=j+1}^{n} (-1)^k \left( \binom{n-1}{k} + \binom{n-1}{k-1} \right) = (-1)^{j+1} \binom{n-1}{j}. Finally we write log⁡k=∫1kdxx=∑j=1k−1Ij\log k = \int_1^k \frac{dx}{x} = \sum_{j=1}^{k-1} I_j, where Ij=∫01dxx+jI_j = \int_0^1 \frac{dx}{x + j}.

Now we have S:=∑k=1n(−1)k(nk)log⁡k=∑k=1n(−1)k(nk)∑j=1k−1Ij=∑j=1n−1Ij∑k=j+1n(−1)k(nk)=(2)∑j=1n−1Ij(−1)j+1(n−1j)==∫01(∑j=1n−1(−1)j+1(n−1j)1x+j)dx=∫01(1x−∑j=0n−1(−1)j(n−1j)1x+j)dx=(1)=∫01(1x−(n−1)!x(x+1)…(x+(n−1)))dx=∫01dxx(1−1(1+x)(1+x/2)…(1+x/(n−1))).\begin{align*} S := \sum_{k=1}^{n} (-1)^k \binom{n}{k} \log k &= \sum_{k=1}^{n} (-1)^k \binom{n}{k} \sum_{j=1}^{k-1} I_j = \sum_{j=1}^{n-1} I_j \sum_{k=j+1}^{n} (-1)^k \binom{n}{k} \overset{(2)}{=} \sum_{j=1}^{n-1} I_j (-1)^{j+1} \binom{n-1}{j} = \\ &= \int_0^1 \left( \sum_{j=1}^{n-1} (-1)^{j+1} \binom{n-1}{j} \frac{1}{x + j} \right) dx = \int_0^1 \left( \frac1x - \sum_{j=0}^{n-1} (-1)^j \binom{n-1}{j} \frac{1}{x + j} \right) dx \overset{(1)}{=} \\ &= \int_0^1 \left( \frac1x - \frac{(n-1)!}{x (x+1) \dots (x + (n-1))} \right) dx = \int_0^1 \frac{dx}{x} \left( 1 - \frac{1}{(1 + x)(1 + x/2) \dots (1 + x/(n-1))} \right). \end{align*} So SS is again expressed as an integral, for which it is not hard to get an asymptotics.

Since et⩾1+te^t \geqslant 1 + t for all real tt (by convexity or any other reason), we have ey2−y⩾1+y2−y=1+y31+y⩾11+ye^{y^2 - y} \geqslant 1 + y^2 - y = \frac{1 + y^3}{1 + y} \geqslant \frac{1}{1 + y} and 11+y⩾1ey=e−y\frac{1}{1 + y} \geqslant \frac{1}{e^y} = e^{-y} for y>0y > 0. Therefore ey2−y⩾11+y⩾e−y,y>0.e^{y^2 - y} \geqslant \frac{1}{1 + y} \geqslant e^{-y}, \quad y > 0. Using this double inequality we get ex2(1+122+⋯+1(n−1)2)−x(1+12+⋯+1n−1)⩾1(1+x)(1+x/2)…(1+x/(n−1))⩾e−x(1+12+⋯+1n−1).e^{x^2 \left( 1 + \frac{1}{2^2} + \dots + \frac{1}{(n-1)^2} \right) - x \left( 1 + \frac12 + \dots + \frac{1}{n-1} \right)} \geqslant \frac{1}{(1 + x)(1 + x/2) \dots (1 + x/(n-1))} \geqslant e^{-x \left( 1 + \frac12 + \dots + \frac{1}{n-1} \right)}. Since x2(1+1/22+… )⩽2x2⩽2xx^2 (1 + 1/2^2 + \dots) \leqslant 2 x^2 \leqslant 2x, we conclude that 1(1+x)(1+x/2)…(1+x/(n−1))=e−Cnx,where −2+∑j=1n−11j⩽Cn⩽∑j=1n−11j,\frac{1}{(1 + x)(1 + x/2) \dots (1 + x/(n-1))} = e^{-C_n x}, \quad \text{where } -2 + \sum_{j=1}^{n-1} \frac1j \leqslant C_n \leqslant \sum_{j=1}^{n-1} \frac1j, i.e., Cn=log⁡n+O(1)C_n = \log n + O(1). Thus S=∫01(1−e−Cnx)dxx=∫0Cn(1−e−t)dtt=∫1Cndtt+∫01(1−e−t)dtt+∫1Cne−tdtt=log⁡Cn+O(1)=log⁡log⁡n+O(1).S = \int_0^1 (1 - e^{-C_n x}) \frac{dx}{x} = \int_0^{C_n} (1 - e^{-t}) \frac{dt}{t} = \int_1^{C_n} \frac{dt}{t} + \int_0^1 (1 - e^{-t}) \frac{dt}{t} + \int_1^{C_n} e^{-t} \frac{dt}{t}

= \log C_n + O(1) = \log \log n + O(1).

How the field did

contestants scored
453
average (of 10)
0.09
solved (≥ 80%)
0.2%
near-0 (≤ 10%)
98.5%
discrimination
0.16

Score distribution (field cohort)

Computed on contestants with a meaningful total (field cohort); discrimination is the corrected item–total correlation.

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