Lattice packings through division algebras



Presentation by

Nihar P. Gargava


Doctoral student,
Chair of Number Theory,
Section of Mathematics,
École Polytechnique Fédérale de Lausanne,


Introduction

\(\DeclareMathOperator{\R}{\mathbb{R}}\)\(\DeclareMathOperator{\Z}{\mathbb{Z}}\)\(\DeclareMathOperator{\C}{\mathbb{C}}\)\( \DeclareMathOperator{\F}{\mathbb{F}}\) Suppose we have $\{a_1, a_2, ... , a_n \} \in m \Z$ with $a_i \ge 0 $ and suppose that $$\frac{1}{n}\sum_{i=1}^{n} a_i < m.$$

What can we conclude about the numbers $a_i$?

Answer: At least one of them is $0$.

Lemma

(Integrality gap)
Let $X$ be a probability space and $\Phi: X\to \R^{\ge 0}$ be a random variable that takes values only in $m \Z$ for some integer $m$.
If $\int_X \Phi < m$, then there exists some $x \in X$ such that $\Phi(x) = 0$.

For our application, we will set $X$ to be a set of lattices.

What is a lattice packing?

Consider $\R^d$ with the standard inner product. A lattice $\Lambda\subseteq \R^d$ is a discrete subgroup such that the quotient space $\R^d / \Lambda$ has a finite induced volume.

Given a lattice $\Lambda$, choose $r > 0$ and consider the open balls $\{ B_{r}\left( v \right)\}_{v \in \Lambda}$ such for any $v_1, v_2 \in \Lambda$, $B_{r}(v_1) \cap B_{r}(v_2) \neq \emptyset \Rightarrow v_1 = v_2$.

Then $(\Lambda,r)$ is called a lattice sphere packing, or simply lattice packing inside $(\R^d, \langle \ , \ \rangle)$.

Packing density of a lattice packing is: \[ \lim_{R \rightarrow \infty} \frac{\mu\left( B_{R}(0) \cap \left( \bigsqcup_{v \in \Lambda} B_{r}(v) \right) \right)}{\mu\left( B_{R}(0) \right)} = \frac{\mu( B_{r}(0) )}{ \mu(\R^d / \Lambda)} \] It is always in the interval $]0,1[$.


Visualizing in $\R^2$


However, note that $2r$ can be at most $$m(\Lambda) = \min_{v \in \Lambda \setminus \{0\}} ||v|| ,$$ otherwise some balls will begin to intersect.

The goal is to maximize packing density. So take $r = \frac{1}{2}m(\Lambda)$. In that case packing density will be equal to $$\frac{\mu( B_{m(\Lambda)/2}(0) )}{ \mu(\R^d / \Lambda)},$$ and is independent of scaling.

To maximize this over all $\Lambda$, it is sufficient to maximize over unit covolume lattices (i.e. \(\mu(\R^d/\Lambda)\) = 1)


Visualizing in $\R^2$

So we somehow find \[ \sup_{\substack{\Lambda \subseteq \R^d \\ \mu(\R^d/\Lambda)=1}} \mu\left(B_{m(\Lambda)/2}(0) \right) \]

\[ = \sup_{ g \in SL_d(\R)} \frac{1}{2^d} \mu\left(B_{m(g \Z^d)}(0) \right) \]

which we use to define our dimensional constant $c_d$ \[ \colon= \frac{1}{2^d} c_d \]

Putting it together, we have \[ c_{d} = \sup\left\{ \mu\left( B_{r}(0)\right) \ | \ r> 0,\exists~g \in SL_d(\R) \text{ and } B_{r}(0) \cap g\Z^d = \{ 0\}\right\}. \]

Clearly, \(~c_d \in~]0,2^d[\), so supremum exists!


Visualizing in $\R^2$

What is known about $~c_d$?

The exact value of the constant $c_d$ is known only for $d = \{ 1,2,3,4,5,6,7,8,24\}$. For other $d$, we want to understand the asymptotic behaviour. In this talk, we will only focus on lower bounds.

Some asymptotic lower bounds for large dimensions

Lower bound Contribution of Dimensions covered
$c_d \ge 1$ Minkowksi (1896) $\forall ~d \ge 1$
$c_d \ge 2(d-1)$ Ball (1992) $\forall ~d \ge 1$
$c_{4n} \ge 8.8n$ Vance (2011) $d=4n , n \ge 1$
$c_{2\varphi(n)} \ge n$ Venkatesh (2013) $d=2\varphi(k)$ for some $k$

Since $\lim \inf \left( \frac{\varphi(n)}{n} \log \log n\right) = e^{- \gamma}$, the last bound is the best lower bound (among these, and overall) on $c_d$ in infinitely many dimensions. The first dimension where it outperforms all others in this list is $d=960$.

Explicit lattice constructions can help us only upto $d \le 300$.

(Ball, 1992) , the lower bound is as indicated.

(Vance, 2011) , using a probabilistic argument on lattices that lie in vector-space over quaternion division algebra. Works only when $4 \mid d$.

(Venkatesh, 2013) , using a probabilistic argument on lattices that lie in $(K \otimes_\mathbb{Q} \mathbb{R})^2$, $K$ is a cyclotomic field.

(G., 2021) , using a probabilistic argument on lattices that lie in $(D \otimes_\mathbb{Q} \mathbb{R})^2$, $D$ is a division algebra over $\mathbb{Q}$.


Comparison of bounds

Theorem

(G. 2021)
Let $D$ be a finite-dimensional division algebra over $\mathbb{Q}$. Let $\mathcal{O} \subseteq D$ be an order in $D$ and $G_{0} \subseteq \mathcal{O}$ be a finite group embedded in the multiplicative group of $D$. Then if $d=2\dim_{\mathbb{Q}}D$, then \begin{align} c_{d} \ge \# G_{0}. \end{align}

To recover Venkatesh's result, set $D=\mathbb{Q}(\mu_n)$, $\mathcal{O} = \mathbb{Z}[\mu_n]$ and $G_0 = \langle \mu_n \rangle$. Hence, this gives \begin{align} c_{2 \varphi(n)} \ge n \end{align}

The cherrypicked sequence of Venkatesh achieves an asymptotic growth of $O( d \log \log d )$. This is achieved by setting $K$ as the $n$th cyclotomic field where $n=\prod_{p < N} p$.

The new result provides more freedom to cherrypick sequences. Instead of choosing a sequence of cyclotomic fields, we can now choose sequences of $\mathbb{Q}$-divison algebras. However, no such sequence will be able to give an asymptotic result strictly better than $O( d \log \log d )$. Improvements in individual dimensions is still possible, as shown before.

The probabilistic method

Recall:
To show $c_d \ge M$, we must prove the existence of $g \in SL_d(\R)$ such that the origin centered ball $B$ with $\mu(B) = M$ has $g \Z^d \cap B = \{0\} $

This is an optimization problem on the space \[ X_d:= \{ \Lambda \subset \mathbb{R}^{d} | \ \mu(\mathbb{R}^{d} / \Lambda) = 1 \} = \{ g\Z^d \ | \ g \in SL_d(\R) \} \]

\[ \simeq SL_d(\mathbb{R})/SL_d(\mathbb{Z}). \]

A priori, this is a bijection of sets. But now we can pull back the topology and the measure from $SL_d(\R)/SL_d(\Z)$.

$SL_d(\R)$ has the topology of a locally compact group.

$SL_d(\R)$ is unimodular. $SL_d(\Z)$ is a discrete subgroup inside $SL_d(\R)$ and therefore there is a unique left $SL_d(\R)$-invariant measure on $SL_d(\R)/SL_d(\Z)$.

Proposition


There exists a unique (upto scaling) natural measure on $SL_d(\R)/SL_d(\Z)$, left-invariant under $SL_d(\R)$ action on cosets.
Furthermore, $SL_d(\mathbb{R})/SL_d(\mathbb{Z})$ under this has a bounded total measure.

This gives us a probability space. Hence we can talk about random unit covolume lattices.

Lattice-sum function

Consider a bounded measurable function with compact support $f:\R^d \rightarrow \R$.
e.g. the indicator function of a ball.

With this, we can now construct the lattice-sum function $\Phi_f(\Lambda): X_d \rightarrow \R$, given as \[ \Phi_f(\Lambda) = \sum_{v \in \Lambda \setminus \{ 0\}}^{} f(v).\]

Since we can generate random lattices, we can talk about the expected value of $\Phi_f(\Lambda)$.

Let us try to do this experimentally! Let us sample over a set $S \subseteq X_d$ of lattices.

So we see that it is almost the integral.


Visualizing in $\R^2$

What we are empirically confirming is the following.

Theorem

(Siegel, 1945)
Suppose $f:\mathbb{R}^{d} \rightarrow \mathbb{R}$ is a compactly supported bounded measurable function. Then, the following holds. \begin{align} \int_{X_d} \Phi_f = \int_{SL_d(\mathbb{R})/SL_d(\mathbb{Z})}^{} \left( \sum_{v \in g \mathbb{Z}^{d} \setminus \{ 0\}}^{}f(v) \right) dg = \int_{\mathbb{R}^{d}}^{} f(x) dx, \end{align} where the $dx$ on the right-hand side is the usual Lebesgue measure on $\mathbb{R}^{d}$ and $dg$ is the unique $SL_d(\mathbb{R})$-invariant probability measure on $SL_d(\mathbb{R})/SL_d(\mathbb{Z})$.

But as you saw that for any $\Lambda \in X_d$, when $f$ is the indicator of a ball, we must have $\Phi_f(\Lambda) \in \{0,2,4,6,...\}$. That's because balls are symmetric. $$v \in \text{supp}(f) \cap ( \Lambda \setminus \{0 \}) \Rightarrow -v \in \text{supp}(f) \cap ( \Lambda \setminus \{0 \}).$$

If $f$ is the indicator function of a ball of volume $2- \varepsilon$, then this tells us that for any dimension $d$ $$\int_{X_d} \Phi_f = 2 - \varepsilon$$

Conclusion: There exists some lattice $\Lambda \in X_d$ such that $\Phi_f(\Lambda) = 0$. That is, there is some lattice of unit covolume that intersects trivially with an origin centered ball of volume $ 2 - \varepsilon$.

Another conclusion: $c_d \ge 2$ for all dimensions $d$!

Here we used a symmetry of $\{\pm 1\}$ under which the ball and the lattice are invariant. Both Venkatesh and the new result use this idea.

Venkatesh's approach was to work with larger a larger group, namely $\Z/n\Z$. For the new result, the symmetries are even bigger and non-commutative.

Venkatesh's lower bound

The idea is to take a nice enough subcollection $Y_d \subseteq X_d$ of lattices and average the lattice-sum function $\Phi_f$ over them.

Define the set $Y_d$ as $$Y_d = \left\{ \left[ \begin{smallmatrix} a & b \\ c & d \end{smallmatrix}\right] \mathcal{O}_K^2 \ | \ a,b,c,d \in K_{\R} = K\otimes \R , ad-bc =1_{K_{\R}} \right\}.$$

In conventional notation, this is just $$Y_d = \{ g ( \mathcal{O}_K^{\oplus 2} ) \ | \ g \in SL_2(K_{\R}) \} \simeq SL_2(K_\mathbb{R} ) /SL_2(\mathcal{O}_K). $$

Again, with this bijection, we will make $Y_d$ a topological space. With a bit of work, we can also show that $Y_d$ has a finite measure with respect to the natural left $SL_2(K_{\R})$-invariant measure.

$Y_d$ can be give probability measure. But why make such a $Y_d$?

Note that whenever $a,b,c,d \in {K}_{\R}$, we have that $$ \left[ \begin{matrix} a & b \\ c & d \end{matrix}\right]\left[ \begin{matrix} \mu_n & 0 \\ 0 & \mu_n \end{matrix}\right]=\left[ \begin{matrix} \mu_n & 0 \\ 0 & \mu_n \end{matrix}\right]\left[ \begin{matrix} a & b \\ c & d \end{matrix}\right]$$

And if $x,y \in \mathcal{O}_K$ and if $r \notin n \Z$, we have that $$ \left[ \begin{matrix} \mu_n^r & 0 \\ 0 & \mu_n^r \end{matrix}\right]\left[ \begin{matrix} x \\ y \end{matrix}\right]=\left[ \begin{matrix} x \\ y \end{matrix}\right] \Rightarrow x=y=0.$$

Putting the two together implies that for each $g\in SL_2(K_{\mathbb{R}})$, $g(\mathcal{O}_K^{\oplus 2})$ is invariant under the cyclic group action of $\langle \mu_n \rangle$, and the action has full orbits on the non-zero points of the lattice.

So if $f$ is the indicator function of an origin-centered ball with respect to a quadratic form that is invariant under $\langle \mu_n \rangle$, we have that $\Phi_f(\Lambda) \in \{0,n,2n,3n,4n,\ldots\}$ for $\Lambda \in Y_d$. Such a quadratic form always exists by averaging!

Venkatesh proves the following analogue of Siegel's theorem.

Theorem

(Venkatesh 2013)
Let $d = 2\varphi(n)$. Suppose $f:K_{\mathbb{R}}^2 \rightarrow \mathbb{R}$ is a compactly supported bounded measurable function. Then, the following holds. \begin{align} \int_{Y_d} \Phi_f = \int_{SL_2(K_\mathbb{R})/SL_2(\mathcal{O}_K)}^{} \left( \sum_{v \in g \mathcal{O}_K^{\oplus 2} \setminus \{ 0\}}^{}f(v) \right) dg = \int_{\mathbb{R}^{d}}^{} f(x) dx, \end{align} where the $dx$ on the right-hand side is that Lebesgue measure on $\mathbb{R}^{d}$ that makes $\mathcal{O}_K^{\oplus 2}$ of unit covolume and $dg$ is the unique $SL_2(K_\mathbb{R})$-invariant probability measure on $Y_d$.

Conclusion: By setting $f$ as the indicator function of a ball in a suitable quadratic form, we conclude that there exists some lattice $\Lambda \in Y_d$ such that $\Phi_f(\Lambda) = 0$. That is, there is some lattice of unit covolume that intersects trivially with an origin centered ball of volume $ n - \varepsilon$.

Another conclusion: $c_{2 \varphi(n)} \ge n$ for all $n$!

Using division algebras

The division algebra case is on similar lines. Let $D$ be a finite-dimensional division algebra over $\mathbb{Q}$. Let $\mathcal{O} \subseteq D$ be an order in $D$. We work in $d = 2 \dim_{\mathbb{Q}} D$ dimensions. Define $D_{\R}= D \otimes_\mathbb{Q} \R$. $$Y_d = \{ g ( \mathcal{O}^{\oplus 2} ) \ | \ g \in SL_2(D_{\R}) \} \simeq SL_2(D_\mathbb{R} ) /SL_2(\mathcal{O}). $$
Here $$ SL_2(D_{\R}) = \left\{ \left[ \begin{matrix} a & b \\ c & d \end{matrix}\right] \ | \ \left[ \begin{matrix} x \\ y \end{matrix}\right] \mapsto \left[ \begin{matrix} ax + by \\ cx + dy \end{matrix}\right] \text{ is a measure preserving map on } D_{\R}^{\oplus 2} \right\}$$

$Y_d$ consists of lattices that are invariant under diagonal right-multiplication by units in $\mathcal{O}$ $$ g ( \mathcal{O}^{\oplus 2} ) = g ( \mathcal{O}^{\oplus 2} ) \left[ \begin{matrix} \mu & 0 \\ 0 & \mu \end{matrix}\right] , \text{ for any } \mu \in \mathcal{O}^{*} $$

And if $x,y \in \mathcal{O}$ and if $ \mu \in \mathcal{O}^{*} \setminus \{ 1_{D}\}$, we have that $$\left[ \begin{matrix} x \\ y \end{matrix}\right] \left[ \begin{matrix} \mu & 0 \\ 0 & \mu \end{matrix}\right]=\left[ \begin{matrix} x \\ y \end{matrix}\right] \Rightarrow x=y=0.$$

Theorem

(G. 2021)
Let $d = 2\varphi(n)$. Suppose $f:D_{\mathbb{R}}^2 \rightarrow \mathbb{R}$ is a compactly supported bounded measurable function. Then, the following holds. \begin{align} \int_{Y_d} \Phi_f = \int_{SL_2(D_\mathbb{R})/SL_2(\mathcal{O})}^{} \left( \sum_{v \in g \mathcal{O}^{\oplus 2} \setminus \{ 0\}}^{}f(v) \right) dg = \int_{\mathbb{R}^{d}}^{} f(x) dx, \end{align} where the $dx$ on the right-hand side is that Lebesgue measure on $\mathbb{R}^{d}$ that makes $\mathcal{O}^{\oplus 2}$ of unit covolume and $dg$ is the unique $SL_2(D_\mathbb{R})$-invariant probability measure on $Y_d$.

To get packing bounds, fix a finite subgroup $G_0 \subseteq \mathcal{O}^*$ to act diagonally on the right of $\mathcal{O}^{\oplus 2}$. Choose $f$ to be the indicator function of a ball invariant under the right action of $G_0$. Then $\Phi_f(\Lambda) \in \{0, |G_0|, 2|G_0|, \dots\}$ for all $\Lambda \in Y_d$.

Conclusion: We get the bounds $$c_{2 \dim_{\mathbb{Q}} D} \ge \# G_0.$$

Let $(G_{0},\mathcal{O}, D ) $ be as finite subgroup inside an order inside a $\mathbb{Q}$-division algebra respectively. Then there exists a lattice packing in dimensions $d= 2\dim_{\mathbb{Q}} D$ whose packing efficiency is at least $\frac{1}{2^{d}} ( \#G_{0} )$.

Note that the following tightening can be done once we have a tuple $(G_{0},\mathcal{O},D)$. When $D$ is a $\mathbb{Q}$-division algebra, the $\mathbb{Q}$-span of $G_{0}$ in $D$ is also a division algebera. Let $\mathbb{Z}\langle G_{0}\rangle \subseteq \mathcal{O}$ be the $\mathbb{Z}$-span of $G_{0}$, then we get that $(G_{0},\mathbb{Z}\langle G_{0}\rangle ,\mathbb{Q}\langle G_{0}\rangle )$ is another tuple that gives us a lower bound.

So we only need to find finite subgroups $G_0$ that live in some $\mathbb{Q}$-division algebra $D$ and we can get the rest.

Fortunately, there exists a complete classification of such finite subgroups due to Amitsur, 1955.

Summary of Amitsur's result

The following construction $D=(E/F,\sigma,\gamma)$ is called a cyclic division algebra.

  • $\DeclareMathOperator{\Gal}{Gal}$ $\DeclareMathOperator{\N}{N}$ $F$ is a number field over $\mathbb{Q}$,
  • $E/F$ is a cyclic extension of degree $n$, i.e. the field extension $E/F$ is Galois and the Galois group is cyclic,
  • $\sigma$ is a generator of the cyclic group $\Gal(E/F)$ and
  • $\gamma \in F^{*}$, with the property that the multiplicative order of $\gamma$ in the group $K^{*}/ N_{F}^{E}(E^{*})$ is exactly $n$. That is, $\gamma^{k} \notin N_{F}^{E}(E^{*})$ for any $k \in \{ 1,2,\dots,n-1\}$ and $\gamma^{n} = N_{F}^{E}(x)$ for some $x\in E^{*}$.
    When this happens we say that $\gamma \in F^{*}$ is a non-norm element.

Consider a formal element $b$ that does not commute with $E$ and satisfies $b^{n} = \gamma$. $D$ is now defined as per the isomorphism \begin{align} \label{eq:iDentify} D \simeq E \oplus E b \oplus E b^{2} \oplus \dots \oplus E b^{n-1},\end{align} with the rule that \begin{align} bl = \sigma(l)b \text{ for all }l \in E.\label{eq:ruleofD} \end{align}

If $\gamma$ does not satisfy the non-norm condition, then we call $(E/F,\sigma,\gamma)$ just a cyclic algebra.

All division algebras over $\mathbb{Q}$ are cyclic division algebras!

The dimension of $D=(E/F,\sigma,\gamma)$ over $\mathbb{Q}$ is $\dim_{\mathbb{Q}}D = [F:\mathbb{Q}]n^2 = [E:\mathbb{Q}]n$.

For $1 \le r \le m $ and $(m,r)=1$, consider the integers

  • $\DeclareMathOperator{\ord}{ord} n = \ord_{m} r$ is the multiplicative order or $r$ modulo $m$, that is the smallest postive integer $k$ such that $m \mid r^{k}-1$,
  • $s = \gcd(r-1,m)$,
  • $t = m/s$.

When $r=1$, we will assume $n=s=1$. In these definitions, we will think of $m$ and $r$ as two parameters and $n,s,t$ will automatically be set as defined above.

With this, consider the cyclic algebra $\mathfrak{U}_{m,r} = (\mathbb{Q}(\mu_{m}), F , \sigma_{r}, \mu_{m}^{t} )$, where $F$ is the subfield of $\mathbb{Q}(\mu_{m})$ fixed by $\sigma_{r}$, and $\sigma_{r}$ is the field automorphism of $\mathbb{Q}(\mu_{m})$ given by $\mu_{m} \mapsto \mu_{m}^{r}$.

A priori, $\mathfrak{U}_{m,r}$ is just a $\mathbb{Q}$-algebra which may not be a division algebra. For this to be a division algebra, we want that $\mu_{m}^{t}$ is a non-norm element of $F$.

Theorem

(Amitsur, 1955)
Consider the following conditions on the numbers $m,r,n,s,t$ defined above. Then $\mathfrak{U}_{m,r}$ is a division algebra if and only if both of the following hold.
  • One of the following two conditions hold.
    • $\gcd(n,t) = 1$. This implies that $\gcd(s,t) = 1$.
    • $n=2n', m=2^{\alpha} m',s=2s'$, for some $\alpha \ge 2$ and $m',s',n'$ are odd numbers, such that $\gcd(n,t)=\gcd(s,t)=2$ and $ 2 ^{\alpha} \mid (r + 1) $.
  • One of the following two conditions hold.
    • $n=s=2$ and $ m \mid (r +1)$.
    • For every prime $ q \mid n$ there exists a prime $p \mid m$ such that if $m = p^{\alpha} m'$ with $p \nmid m'$, we get $q \nmid \ord_{m'} r $. In addition, at least one of the following must hold regarding $p,q$.
      • $p \neq 2$ and $\gcd(q,\frac{ p^{ \delta} - 1}{s} ) = 1$, where $\delta = \ord_{m'} p$.
      • $p=q=2$ and $m/4 \equiv \delta \equiv 1 \pmod{2}$, where $\delta$ is as above.

The division algebra $\mathfrak{U}_{m,r}$ contains the following group.

\begin{align} G_{m,r}:= \langle A,B \ | \ A^{m} = 1, B^{n} = A^{t}, BAB^{-1} = A^{r}\rangle. \end{align} We get that $|G_{m,r}|= mn = m \ord_{m}r$. When $r=1$, $G_{m,r}$ is a cyclic group of order $m$.

It embeds as per $i:G_{m,r} \rightarrow \mathfrak{U}_{m,r}^{*}$ defined sending $A \mapsto \mu_{m}$ and $B \mapsto b$.

When $\ord_m 2$ is odd, one can get a slightly better group of size $24 |G_{m,r}|$ embedded in the division algebra $\left(\tfrac{-1,-1}{\mathbb{Q}}\right) \otimes_{\mathbb{Q}} \mathfrak{U}_{m,r}$.

Amitsur then proves that apart from two more sporadic examples, these two are the only families of examples.

Theorem

(Amitsur, 1955)
The following is an exhaustive list of finite groups \(G_{0}\) that can be embedded in some \(\mathbb{Q}\)-division algebra \(D\).
Group structure Conditions on the parameters Size of the group Dimension of the smallest division algebra containing the group
\(G_{0} \subseteq D^{*}\) \(\#G_{0}\) \(\dim_{\mathbb{Q}} \mathbb{Q}\langle G_{0}\rangle\)
\(\mathfrak{D}^{*}\) \(48\) \(16\)
\(\mathfrak{I}^{*}\) \(120\) \(20\)
\(G_{m,r}\) \( (m,r) = 1, \) \( \mathfrak{U}_{m,r}\) is a division algebra \(m\ord_{m} r\) \(\varphi(m) \ord_{m} r\)
\(\mathfrak{T}^{*} \times G_{m,r}\) \(\ord_{m} 2\) is odd and the rest as above 24 \(m\ord_{m} r\) 4 \(\varphi(m) \ord_{m} r\)

Here \(\mathfrak{T}^{*}, \mathfrak{D}^{*}, \mathfrak{I}^{*}\) are the binary tetrahedral group, binary octahedral group and binary icosahedral group respectively. They are finite groups whose respective size is 24,48 and 120.

Note that in the two families, $ \# G_0 / \dim_{\mathbb{Q}} \mathbb{Q} \langle G_0 \rangle = m/\varphi(m) $ or $6 m / \varphi(m)$. Looking at this, we can conclude that along no sequence of new constructions will we get $c_d > O(d \log \log d)$.

We can leverage the second family to get a sequence that shows that $c_d \ge O(d (\log \log d)^{7/24})$

$\DeclareMathOperator{\ord}{ord}$ We pick $$m = \prod_{ \substack{ p \text{ is prime} \\ p \le x \\ 2 \nmid \ord_{p} 2} }^{} p$$ and $r=1$. Then observe that with this, we get that $m$ is odd and $\ord_{m}2$ is also odd. Using the classification just given before, we get that $c_{8\varphi(m)} \ge 24m$. This sequence was the one picturized previously.

The following is also an interesting sequence made of new examples.

Let $\{ p_1,p_2,\dots\}$ be the sequence of all primes and suppose $q= 1+\prod_{i=1}^{N}p_{i} $ is a prime for some $N$. Then we can choose an integer $r$ such that $r \equiv 1 \pmod{p_i}$ for each $i$ but has $\ord_{q} r = q-1$, i.e. $r$ is a generator of $\mathbb{F}_{q}^{*}$.

Set $m=q(q-1) = q \prod_{i=1}^{N} p_i$. Then, one can check that $\mathfrak{U}_{m,r}$ is a division algebra so we can have $$ c_{2 (q-1)^{2}\varphi(q-1) } \ge q(q-1)^{2}.$$ If there are infinitely many such primes, then this sequence asymptotically approaches $O(d\log \log d)$.

Summary: Amitsur's work allows us to conclude that there are no asymptotic improvements possible when we allow some non-commutative symmetries.

Effectiveness

These results prove existence of a lattice with a high packing density. Can we actually construct such lattices algorithmically?

Theorem

(G., Vlad Serban, 2021)
Let $D$ be a finite-dimensional division algebra over $\mathbb{Q}$. Let $\mathcal{O} \subseteq D$ be an order in $D$ and $G_{0} \subseteq \mathcal{O}$ be a finite group embedded in the multiplicative group of $D$. Then in $d=2\dim_{\mathbb{Q}}D$ dimensional real space, there exists a finite time algorithm to construct a lattice $\Lambda \subseteq \mathbb{R}^d$ such that the packing density of $\Lambda $ is at least \begin{align} \tfrac{1}{2^d} \# G_{0} - \varepsilon, \text{ for every }\varepsilon . \end{align}

The approach is based on methods of Moustrou (2017). The idea is that $\int_{Y_d} \Phi_f$ can be approximated by averaging $\Phi_f$ over a growing family of finitely many lattices.

Thank you for your attention!


Email:
nihar.gargava@epfl.ch

arxiv:
2107.04844

Slides:
nihargargava.com/divalg_seminar

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Appendix: How to generate random 2-dimensional lattices


To the map $\psi:[\pi/3,2\pi/3]\times ]0,1] \rightarrow \mathbb{H}$ given by $\psi(a,b) = \cos(a) + i \sin(a)/b$ is a measure preserving map!

It maps the rectangle bijectively to a fundamental domain of $\mathbb{H}/SL_2(\mathbb{Z})$.

Using this, the following map randomly generates a lattice. \[ \psi_1: [0,2\pi]\times [\pi/3,2\pi/3] \times ] 0,1] \to SL_2(\mathbb{R})\\ \psi_1(x,y,z) = \left[ \begin{smallmatrix} 1 & \cos(y) \\ 0 & 1 \end{smallmatrix} \right] \left[ \begin{smallmatrix} \left(\frac{\sin(y)}{z}\right)^{\frac{1}{2}} & 0 \\ 0 & \left({\frac{\sin(y)}{z}}\right)^{-\frac{1}{2}} \end{smallmatrix} \right] \left[ \begin{smallmatrix} \cos(x) & \sin(x) \\ -\sin(x) & \cos(x) \end{smallmatrix} \right] \]

This only works for $d=2$. It is not known how to generalize this to higher dimensions!