# Evaluate $\lim\limits_{n \to \infty}\frac{(-1)^n}{n!}\int_0^n (x-1)(x-2)\cdots(x-n){\rm d}x$

Evaluate $$\lim\limits_{n \to \infty}\frac{(-1)^n}{n!}\int_0^n (x-1)(x-2)\cdots(x-n){\rm d}x\,.$$

\begin{align*} I_n:&=\frac{(-1)^n}{n!}\int_0^n\prod_{k=1}^{n}(x-k){\rm d}x=\frac{(-1)^n}{n!}\int_0^n\prod_{k=1}^{n}(n-k-x){\rm d}x\\&=\frac{(-1)^n}{n!}\int_0^n\prod_{k=0}^{n-1}(k-x){\rm d}x=\frac{1}{n!}\int_0^n\prod_{k=0}^{n-1}(x-k){\rm d}x\\ &=\int_0^n\binom{x}{n}{\rm d}x, \end{align*}

but it's hard to go forward.

• @Ty. I think you didn't understand that he did a change of variable $x \mapsto n - x$. Jul 4, 2020 at 14:57
• The function peaks close to the origin where it behaves as something like $\sim e^{-\log(n) x}$ and will be close to zero otherwise so I would expect the integral to behave as $\mathcal{O}(1/\log(n))$. Suspect if you multiply $I_n$ by $\log(n)$ then you might get a finite non-zero limit. Jul 4, 2020 at 15:14

This supplements the answer by @WhatsUp with a detailed asymptotics of $$I_n$$ in terms of $$\log n$$.

$$I_n=A_n+B_n$$ where $$A_n=\int_0^1 f_n(x)\,dx$$, $$B_n=\int_1^n f_n(x)\,dx$$, and $$f_n(x)=\prod_{k=1}^{n}(1-x/k)$$. Since the answer shows that $$B_n=\mathcal{O}(1/n)$$ as $$n\to\infty$$, let's focus on $$A_n$$. We use (see  and ) $$\prod_{k=1}^\infty\left(1-\frac{x}{k}\right)e^{x/k}=\frac{e^{\gamma x}}{\Gamma(1-x)};\qquad H_n:=\sum_{k=1}^{n}\frac1k=\log n+\gamma+\mathcal{O}(1/n)$$ as $$n\to\infty$$ (we omit this remark in the sequel). Further, $$\prod_{k=n+1}^\infty(1-x/k)e^{x/k}=1+\mathcal{O}(1/n)$$ uniformly for $$0\leqslant x\leqslant 1$$ (this boils down to showing $$\sum_{k=n+1}^\infty k^{-2}\asymp 1/n$$), which implies $$f_n(x)=e^{-H_n x}\prod_{k=1}^{n}\left(1-\frac{x}{k}\right)e^{x/k}=\frac{e^{(\gamma-H_n)x}}{\Gamma(1-x)}\big(1+\mathcal{O}(1/n)\big)=\frac{n^{-x}}{\Gamma(1-x)}\big(1+\mathcal{O}(1/n)\big),$$ again uniformly for $$0\leqslant x\leqslant 1$$. Thus, $$A_n=g(\log n)+\mathcal{O}(1/n),\qquad g(\lambda)=\int_0^1\frac{e^{-\lambda x}\,dx}{\Gamma(1-x)}.$$ To get the asymptotics of $$g(\lambda)$$, Watson's lemma is applicable. For this, we need the power series of $$1/\Gamma(1-x)$$, which can be obtained (algorithmically at least) from the known series $$\log\Gamma(1-x)=\gamma x+\sum_{k=2}^\infty\frac{\zeta(k)}{k}x^k.$$ Thus, $$I_n\asymp\frac{1}{\log n}-\frac{\gamma}{(\log n)^2}+\frac{\gamma^2-\pi^2/6}{(\log n)^3}+\ldots\qquad(n\to\infty)$$ (as the asymptotics is in powers of $$1/\log n$$, all the $$\mathcal{O}(1/n)$$ terms can be simply neglected).

• Just a remark: The $A_n$'s are related to the Bernoulli number of the second kind (or Gregory coefficients): en.wikipedia.org/wiki/Gregory_coefficients
– Gary
Jul 4, 2020 at 17:14
• @Gary: The Gregory coefficients are $\int_0^1\binom{x}{n}\,dx$, but $A_n=\int_{n-1}^n\binom{x}{n}\,dx$. Jul 4, 2020 at 17:17
• Ok, I see it now.
– Gary
Jul 4, 2020 at 17:23
• +1 and I learnt a new thing called Watson's lemma. Jul 5, 2020 at 1:51
• If $n$ is odd, then the substitution $x - (n + 1)/2 = u$ converts the integral from 1 to $n$ to $\int_{(1-n)/2}^{-(1-n)/2} u(u^2 - 1)\cdots\left(u^2 - ((n - 1)/2)^2\right)\,du = 0$ since $\int_{-a}^a f(x)dx = 0$ when $f$ is an odd function. Jul 7, 2020 at 18:10

Let $$I_{n, d}$$ be the integral $$\int_{d - 1}^d\binom x n dx$$. Then your $$I_n$$ is just $$\sum_{d = 1}^n I_{n, d}$$.

We are going to treat the terms $$I_{n, d}$$ in several cases.

As a first observation, note that on the interval $$[d - 1, d)$$, we have: $$\begin{eqnarray} n!\left|\binom x n\right| &=& x \cdot (x - 1)\cdots (x - (d - 1)) \cdot (d - x) \cdot (d + 1 - x) \cdots (n - 1 - x)\\ &\leq& d \cdot (d - 1) \cdots 1 \cdot 1\cdot 2 \cdots (n - d)\\ &=& d! (n - d)! \end{eqnarray}$$ which gives $$\left|\binom x n\right| \leq \binom n d ^{-1}$$.

Now for our $$I_{n, d}$$, we have the following cases.

Case 1: $$d = 1$$ or $$d = n - 1$$.

For these two $$d$$, we have $$\left|\binom x n\right| \leq n^{-1}$$, hence $$I_{d, n} \leq n^{-1}$$. Taking limit yields $$\lim_{n\rightarrow \infty} I_{n, 1} + I_{n, n - 1} = 0$$.

Case 2: $$2\leq d \leq n - 2$$.

In this case, we have $$\left|\binom x n\right| \leq \frac 2{n(n - 1)}$$ for sufficiently large $$n$$ (e.g. $$n \geq 4$$). Therefore we can bound the sum: $$\sum_{d = 2}^{n - 2} \left|I_{n, d}\right|\leq (n - 3)\cdot \frac 2{n(n - 1)}.$$ Taking limit again gives $$\lim_{n \rightarrow\infty} \sum_{d = 2}^{n - 2} I_{n, d} = 0$$.

Case 3: $$d = n$$.

This is the only remaining case. We write $$J_n$$ for $$I_{n, n}$$ and rewrite the integral as $$J_n = \int_{n - 1}^n\binom x n dx = \int_0^1 \frac{x \cdot (x + 1) \cdots (x + n - 1)}{n!}dx.$$

Write $$g_n(x) = \frac{x \cdot (x + 1) \cdots (x + n - 1)}{n!}$$. Obviously we have $$0\leq g_n(x) \leq 1$$ for all $$x\in[0, 1]$$.

Furthermore, it is a simple exercise, based on the divergence of the harmonic series, that $$\lim_{n\rightarrow \infty} g_n(x) = 0$$ for any $$x\in[0, 1)$$.

Now the magic happens by applying the dominated convergence theorem. It tells us that the limit $$\lim_{n\rightarrow\infty} J_n$$ is $$0$$.

Combining all three cases, we get $$\lim_{n\rightarrow\infty} I_n = 0$$.

• @Downvoters Please consider leaving a comment mentioning the reason for the downvote. Jul 4, 2020 at 16:18
• +1 Sometimes they downvote by msitake Jul 4, 2020 at 19:24