A lattice is a discrete additive subgroup of $\newcommand{\R}{\mathbb{R}}\R^n$; for the purposes of this post we’ll restrict ourselves to full rank lattices, i.e., those which span the entire vector space $\R^n$. An alternate way of defining a lattice is to consider it as the integer span of a collection of linearly independent vectors $\newcommand{\b}{\mathbf{b}}\newcommand{\c}{{,}~}\b_1\c\dotsc\c\b_n\in\R^n$, known as a basis of $L$. That is, $L$ consists of the collection of points

\[ \newcommand{\norm}[1]{\lVert#1\rVert}\newcommand{\x}{\mathbf{x}}\newcommand{\Z}{\mathbb{Z}} \biggl\{\,\sum_{i=1}^n x_i\b_i : x_i\in\Z \,\biggr\} . \]

The goal of this post is to show that these two ways of defining a lattice are equivalent. It is more or less immediate that a lattice $L$ in the second sense is an additive subgroup which spans $\R^n$; it is also discrete since one can show that¹

\[ \newcommand{\0}{\mathbf{0}} \norm{\x} \geq \min_{1\leq i\leq n}\norm{\b_i^*} \]

for all $\x\in L$ and $\x\neq\0$, where $\b_i^*$ is the Gram–Schmidt orthogonalization of $\b_i$. Thus $\0$ is an isolated point of $L$, and it follows that every point of $L$ must be isolated; a nonisolated point would imply (using a suitable translation) that $\0$ is nonisolated.

The harder direction is to show that a lattice in the first sense is also a lattice in the second sense; the remainder of the post is devoted to this. Let $L$ be a discrete additive subgroup of $\R^n$ containing $n$ linearly independent vectors.

In the case $n:=1$, since $L$ spans $\R$ it must contain $\pm a\neq0$. Since it also contains $0$ and is discrete, there must be a minimal $b>0$ with $b\in L$. Then as required we have $L= \{\, xb : x\in\Z \,\}$; if there was any point $c=xb\in L$ for $x\notin\Z$ then $c-\lfloor x\rfloor b$ would lie in $L$ and $(0,b)$, a contradiction to the minimality of $b$.

Now suppose the result holds for all lattices in $\R^{n-1}$; we will show the result holds for $L$ and appeal to induction. We know that $L$ contains $n$ linearly independent vectors; select any $n-1$ of them and let $S$ be the subspace they generate. Let $S’$ be the rotation of $S$ such that every vector in $S’$ has a $0$ in its final coordinate. Crucially, applying the rotation on $L$ to form $L’$ preserves the discrete additive subgroup structure, so we can apply the induction hypothesis to the discrete additive subgroup formed by only considering the first $n-1$ coordinates of $S’\cap L’$. Let $\b_1\c\dotsc\c\b_{n-1}$ be a basis of this lattice (which exists by hypothesis), except extended with an extra $0$ coordinate so as to live in $\R^n$ instead of $\R^{n-1}$.

Since $L’$ generates $\R^n$, it must contain a vector not in $S’$, i.e., with nonzero final coordinate. Furthermore, it must contain a vector with minimal positive final coordinate; otherwise there would exist a sequence $\newcommand{\N}{\mathbb{N}}\{\x_i\in L’\}_{i\in\N}$ with $(x_i)_n\to0$ as $i\to\infty$. By translating the $\x_i$ by suitable multiples of $\b_1\c\dotsc\c\b_{n-1}$ we can ensure that they lie in the compact set

\[ \biggl\{\, \sum_{i=1}^{n-1} \alpha_i \b_i + (0,\dotsc,0,\alpha) : \alpha_i\in[0,1]\c\lvert \alpha\rvert\leq\max_{i\in\N}\lvert(x_i)_n\rvert \,\biggr\} , \]

and therefore some subsequence of the $\x_i$ converges to some point in the set. Taking successive differences of this subsequence we get a sequence of points in $L’$ which converge to $\0\in L’$, a contradiction to the discreteness of $L’$.

Let $\b_n\in L’$ be a vector with minimal nonzero final coordinate. We claim that $\b_1\c\dotsc\c\b_n$ is a basis for $L’$. Let $\x\in L’$ be arbitrary, and consider

\[ \x’ := \x – \biggl\lfloor \frac{x_n}{(b_n)_n} \biggr\rfloor \b_n \in L’ . \]

Its final coordinate is $x_n-\lfloor x_n/(b_n)_n\rfloor(b_n)_n\in[0,(b_n)_n)$ and therefore by minimality of $(b_n)_n$ it must be $0$, so $\x’\in S’\cap L’$ and therefore can be written as an integer combination of $\b_1\c\dotsc\c\b_{n-1}$. Thus $\x=\x’+\lfloor x_n/(b_n)_n\rfloor\b_n$ can be written as an integer linear combination of $\b_1\c\dotsc\c\b_n$, and so these vectors form a basis of $L’$. Applying the reverse rotation of $L\mapsto L’$ to the basis $\b_1\c\dotsc\c\b_n$ gives us a basis of $L$, as required.

Last time when I derived the formula for the volume of a hypersphere in $n$ dimensions I forgot to point out a curious consequence of the formula, namely that the volume tends to zero as $n$ tends to infinity.

When I was an undergraduate I remember a professor of mine pointing this out and then declaring “That doesn’t make sense!”. At the time it didn’t seem too surprising to me, since I could see that the unit circle in $\newcommand{\R}{\mathbb{R}}\R^2$ took up more of the surrounding square $[-1,1]^2$ than the unit sphere in $\R^3$ took up of $[-1,1]^3$. Consquently, I thought it likely that the ratio of the volume of the unit sphere in $\R^n$ to the volume of $[-1,1]^n$ should go to zero as $n\to\infty$.

However, I misunderstood the claim being made: not only does the above ratio of hypersphere-to-hypercube volume go to zero, the volume of the hypersphere itself goes to zero. This was something I hadn’t even considered: since as $n\to\infty$ the hypersphere is “growing”, I presumably took for granted that its volume should go to infinity, not zero!

Of course, one can consider the unit sphere in $\R^{n-1}$ as a subset of the unit sphere in $\R^n$, since for example the unit sphere in $\R^3$ contains the unit circle as a “slice”. In this way as $n\to\infty$ the hypersphere is growing. However, though the “slice” has volume in $\R^{n-1}$, it has no volume in $\R^n$; as the dimension increases it becomes “harder” to make volume in a sense. This allows the hypersphere to “grow” as $n\to\infty$ while still shrink in volume.

Algebraically, as we’ve seen, the volume of the unit sphere in $\R^n$ is given by

\[ V_n = \frac{\pi^{n/2}}{(n/2)!} . \]

If one knows Stirling’s approximation

\[ n! \sim \sqrt{2\pi n}\Bigl(\frac{n}{e}\Bigr)^n \]

then it isn’t too hard to see that the denominator of $V_n$ grows asymptotically faster than the numerator, and therefore $V_n$ tends to $0$. Explicitly, we have

\[ \lim_{n\to\infty} V_n = \lim_{n\to\infty}\frac{\pi^{n/2}}{\sqrt{\pi n}(\frac{n}{2e})^{n/2}} = \lim_{n\to\infty}\frac{1}{\sqrt{\pi n}}\cdot\lim_{n\to\infty}\Bigl(\frac{2\pi e}{n}\Bigr)^{n/2} = 0 \]

since $\lim_{n\to\infty}1/\sqrt{\pi n}=0$ and

\[ \lim_{n\to\infty}\Bigl(\frac{2\pi e}{n}\Bigr)^{n/2} = \lim_{n\to\infty}\exp\Bigl(\frac{n}{2}\ln\Bigl(\frac{2\pi e}{n}\Bigr)\Bigr) = \lim_{m\to-\infty}\exp(m) = 0 . \]

The volume of a hypersphere with radius $R$ in $n$ dimensions is given by the expression¹

\[ V_n(R) = \frac{\pi^{n/2}}{(n/2)!} R^n . \]

We will show this by induction on $n$. The base cases can be checked directly, where we make use of polar coordinates in two dimensions:

\begin{gather*}
V_1(R) = \int_{-R}^R\newcommand{\d}{\,\mathrm{d}}\d x = 2R = \frac{\pi^{1/2}}{(1/2)!} R \\
V_2(R) = \iint\limits_{x_1^2+x_2^2\leq R^2}\d x_2\d x_1 = \int_0^{2\pi}\int_0^R r\d r\d\theta = 2\pi\biggl[\frac{r^2}{2}\biggr]_0^R = \pi R^2
\end{gather*}

Suppose the formula holds in dimension $n-2$. Using this, we will show that the formula holds in dimension $n$:

\begin{align*}
V_n(R) &= \int\limits_{x_1^2\leq R^2}\;\int\limits_{x_1^2+x_2^2\leq R^2}\;\int\limits_{x_1^2+x_2^2+x_3^2\leq R^2}\dotsi\int\limits_{x_1^2+\dotsb+x_n^2\leq R^2}\d x_n\dotsm\d x_1 \\
&= \int\limits_{x_1^2\leq R^2}\;\int\limits_{x_1^2+x_2^2\leq R^2}\;\int\limits_{x_3^2\leq R^2-x_1^2-x_2^2}\dotsi\int\limits_{x_3^2+\dotsb+x_n^2\leq R^2-x_1^2-x_2^2}\d x_n\dotsm\d x_1 \\
&= \int\limits_{x_1^2\leq R^2}\;\int\limits_{x_1^2+x_2^2\leq R^2}V_{n-2}\Bigl(\sqrt{R^2-x_1^2-x_2^2}\Bigr)\d x_2\d x_1 \\
&= \frac{\pi^{n/2-1}}{(n/2-1)!}\iint\limits_{x_1^2+x_2^2\leq R^2}\sqrt{R^2-x_1^2-x_2^2}^{n-2}\d x_2\d x_1 \\
&= \frac{\pi^{n/2-1}}{(n/2-1)!}\int_0^{2\pi}\int_0^R\sqrt{R^2-r^2}^{n-2}r\d r\d\theta \\
&= \frac{2\pi^{n/2}}{(n/2-1)!}\biggl[-\frac{1}{n}\sqrt{R^2-r^2}^n\biggr]_0^R \\
&= \frac{\pi^{n/2}}{(n/2)!} R^n
\end{align*}

By induction, the formula holds for all positive integers $n$.

It is well known¹ that

\[ \sum_{p\leq x}\ln p = x + O\biggl(\frac{x}{e^{c\sqrt{\ln x}}}\biggr) \]

where $c>0$ is a constant and the summation runs over the primes. In fact, under the Riemann hypothesis, one even has

\[ \sum_{p\leq x}\ln p = x + O(x^{1/2+\epsilon}) \]

for any $\epsilon>0$. Since $e^{c\sqrt{\ln x}}$ grows slower than any power of $x$, the second statement gives a better approximation.

A related question, but one I wasn’t familiar with, is to give a similar asymptotic result for the summation with $\ln p$ replaced by $\ln\ln p$. In other words, to estimate the quantity

\[ \sum_{p\leq x}\ln\ln p . \]

To do this, we may employ Abel’s summation formula with

\[ a_n := \begin{cases}
1 & \text{if $n$ is prime} \\
0 & \text{otherwise}
\end{cases} \]

and $\phi(n):=\ln\ln n$. Then we have

\[ \sum_{p\leq x}\ln\ln p = \pi(x)\ln\ln x-\int_2^x\frac{\pi(t)}{t\ln t}\mathrm{d}t . \]

By the prime number theorem we have $\pi(t)=t/\ln t+O(t/\ln(t)^2)$, so

\[ \int_2^x\frac{\pi(t)}{t\ln t}\mathrm{d}t = \int_2^x\frac{\mathrm{d}t}{\ln(t)^2}+O\biggl(\int_2^x\frac{\mathrm{d}t}{\ln(t)^3}\biggr) . \]

By Wolfram Alpha we have that

\[ \int_2^x\frac{\mathrm{d}t}{\ln(t)^2} = \DeclareMathOperator{\li}{li}\li(x)-\frac{x}{\ln x} + O(1) = \frac{x}{\ln(x)^2} + O\biggl(\frac{x}{\ln(x)^3}\biggr) , \]

with the latter equality following from the asymptotic expansion of the logarithmic integral.

It remains to estimate the integral $\int_2^x\ln(t)^{-3}\,\mathrm{d}t$. Actually, this is not entirely straightforward, but a trick is to split the integral into two (around $\sqrt{x}$) and then estimate each, as follows:

\[ \int_2^{\sqrt{x}}\frac{\mathrm{d}t}{\ln(t)^3} + \int_{\sqrt{x}}^x\frac{\mathrm{d}t}{\ln(t)^3} \leq \frac{\sqrt{x}-2}{\ln(2)^3} + \frac{x-\sqrt{x}}{\ln(\sqrt{x})^3} = O\biggl(\frac{x}{\ln(x)^3}\biggr) \]

Putting everything together, we find the result

\[ \sum_{p\leq x}\ln\ln p =\pi(x)\ln\ln x-\frac{x}{\ln(x)^2} +O\biggl(\frac{x}{\ln(x)^3}\biggr) . \]