The wave equation is usually expressed in the form

$\displaystyle \partial_{tt} u - \Delta u = 0$

where ${u \colon {\bf R} \times {\bf R}^d \rightarrow {\bf C}}$ is a function of both time ${t \in {\bf R}}$ and space ${x \in {\bf R}^d}$, with ${\Delta}$ being the Laplacian operator. One can generalise this equation in a number of ways, for instance by replacing the spatial domain ${{\bf R}^d}$ with some other manifold and replacing the Laplacian ${\Delta}$ with the Laplace-Beltrami operator or adding lower order terms (such as a potential, or a coupling with a magnetic field). But for sake of discussion let us work with the classical wave equation on ${{\bf R}^d}$. We will work formally in this post, being unconcerned with issues of convergence, justifying interchange of integrals, derivatives, or limits, etc.. One then has a conserved energy

$\displaystyle \int_{{\bf R}^d} \frac{1}{2} |\nabla u(t,x)|^2 + \frac{1}{2} |\partial_t u(t,x)|^2\ dx$

which we can rewrite using integration by parts and the ${L^2}$ inner product ${\langle, \rangle}$ on ${{\bf R}^d}$ as

$\displaystyle \frac{1}{2} \langle -\Delta u(t), u(t) \rangle + \frac{1}{2} \langle \partial_t u(t), \partial_t u(t) \rangle.$

A key feature of the wave equation is finite speed of propagation: if, at time ${t=0}$ (say), the initial position ${u(0)}$ and initial velocity ${\partial_t u(0)}$ are both supported in a ball ${B(x_0,R) := \{ x \in {\bf R}^d: |x-x_0| \leq R \}}$, then at any later time ${t>0}$, the position ${u(t)}$ and velocity ${\partial_t u(t)}$ are supported in the larger ball ${B(x_0,R+t)}$. This can be seen for instance (formally, at least) by inspecting the exterior energy

$\displaystyle \int_{|x-x_0| > R+t} \frac{1}{2} |\nabla u(t,x)|^2 + \frac{1}{2} |\partial_t u(t,x)|^2\ dx$

and observing (after some integration by parts and differentiation under the integral sign) that it is non-increasing in time, non-negative, and vanishing at time ${t=0}$.

The wave equation is second order in time, but one can turn it into a first order system by working with the pair ${(u(t),v(t))}$ rather than just the single field ${u(t)}$, where ${v(t) := \partial_t u(t)}$ is the velocity field. The system is then

$\displaystyle \partial_t u(t) = v(t)$

$\displaystyle \partial_t v(t) = \Delta u(t)$

and the conserved energy is now

$\displaystyle \frac{1}{2} \langle -\Delta u(t), u(t) \rangle + \frac{1}{2} \langle v(t), v(t) \rangle. \ \ \ \ \ (1)$

Finite speed of propagation then tells us that if ${u(0),v(0)}$ are both supported on ${B(x_0,R)}$, then ${u(t),v(t)}$ are supported on ${B(x_0,R+t)}$ for all ${t>0}$. One also has time reversal symmetry: if ${t \mapsto (u(t),v(t))}$ is a solution, then ${t \mapsto (u(-t), -v(-t))}$ is a solution also, thus for instance one can establish an analogue of finite speed of propagation for negative times ${t<0}$ using this symmetry.

If one has an eigenfunction

$\displaystyle -\Delta \phi = \lambda^2 \phi$

of the Laplacian, then we have the explicit solutions

$\displaystyle u(t) = e^{\pm it \lambda} \phi$

$\displaystyle v(t) = \pm i \lambda e^{\pm it \lambda} \phi$

of the wave equation, which formally can be used to construct all other solutions via the principle of superposition.

When one has vanishing initial velocity ${v(0)=0}$, the solution ${u(t)}$ is given via functional calculus by

$\displaystyle u(t) = \cos(t \sqrt{-\Delta}) u(0)$

and the propagator ${\cos(t \sqrt{-\Delta})}$ can be expressed as the average of half-wave operators:

$\displaystyle \cos(t \sqrt{-\Delta}) = \frac{1}{2} ( e^{it\sqrt{-\Delta}} + e^{-it\sqrt{-\Delta}} ).$

One can view ${\cos(t \sqrt{-\Delta} )}$ as a minor of the full wave propagator

$\displaystyle U(t) := \exp \begin{pmatrix} 0 & t \\ t\Delta & 0 \end{pmatrix}$

$\displaystyle = \begin{pmatrix} \cos(t \sqrt{-\Delta}) & \frac{\sin(t\sqrt{-\Delta})}{\sqrt{-\Delta}} \\ \sin(t\sqrt{-\Delta}) \sqrt{-\Delta} & \cos(t \sqrt{-\Delta} ) \end{pmatrix}$

which is unitary with respect to the energy form (1), and is the fundamental solution to the wave equation in the sense that

$\displaystyle \begin{pmatrix} u(t) \\ v(t) \end{pmatrix} = U(t) \begin{pmatrix} u(0) \\ v(0) \end{pmatrix}. \ \ \ \ \ (2)$

Viewing the contraction ${\cos(t\sqrt{-\Delta})}$ as a minor of a unitary operator is an instance of the “dilation trick“.

It turns out (as I learned from Yuval Peres) that there is a useful discrete analogue of the wave equation (and of all of the above facts), in which the time variable ${t}$ now lives on the integers ${{\bf Z}}$ rather than on ${{\bf R}}$, and the spatial domain can be replaced by discrete domains also (such as graphs). Formally, the system is now of the form

$\displaystyle u(t+1) = P u(t) + v(t) \ \ \ \ \ (3)$

$\displaystyle v(t+1) = P v(t) - (1-P^2) u(t)$

where ${t}$ is now an integer, ${u(t), v(t)}$ take values in some Hilbert space (e.g. ${\ell^2}$ functions on a graph ${G}$), and ${P}$ is some operator on that Hilbert space (which in applications will usually be a self-adjoint contraction). To connect this with the classical wave equation, let us first consider a rescaling of this system

$\displaystyle u(t+\varepsilon) = P_\varepsilon u(t) + \varepsilon v(t)$

$\displaystyle v(t+\varepsilon) = P_\varepsilon v(t) - \frac{1}{\varepsilon} (1-P_\varepsilon^2) u(t)$

where ${\varepsilon>0}$ is a small parameter (representing the discretised time step), ${t}$ now takes values in the integer multiples ${\varepsilon {\bf Z}}$ of ${\varepsilon}$, and ${P_\varepsilon}$ is the wave propagator operator ${P_\varepsilon := \cos( \varepsilon \sqrt{-\Delta} )}$ or the heat propagator ${P_\varepsilon := \exp( - \varepsilon^2 \Delta/2 )}$ (the two operators are different, but agree to fourth order in ${\varepsilon}$). One can then formally verify that the wave equation emerges from this rescaled system in the limit ${\varepsilon \rightarrow 0}$. (Thus, ${P}$ is not exactly the direct analogue of the Laplacian ${\Delta}$, but can be viewed as something like ${P_\varepsilon = 1 - \frac{\varepsilon^2}{2} \Delta + O( \varepsilon^4 )}$ in the case of small ${\varepsilon}$, or ${P = 1 - \frac{1}{2}\Delta + O(\Delta^2)}$ if we are not rescaling to the small ${\varepsilon}$ case. The operator ${P}$ is sometimes known as the diffusion operator)

Assuming ${P}$ is self-adjoint, solutions to the system (3) formally conserve the energy

$\displaystyle \frac{1}{2} \langle (1-P^2) u(t), u(t) \rangle + \frac{1}{2} \langle v(t), v(t) \rangle. \ \ \ \ \ (4)$

This energy is positive semi-definite if ${P}$ is a contraction. We have the same time reversal symmetry as before: if ${t \mapsto (u(t),v(t))}$ solves the system (3), then so does ${t \mapsto (u(-t), -v(-t))}$. If one has an eigenfunction

$\displaystyle P \phi = \cos(\lambda) \phi$

to the operator ${P}$, then one has an explicit solution

$\displaystyle u(t) = e^{\pm it \lambda} \phi$

$\displaystyle v(t) = \pm i \sin(\lambda) e^{\pm it \lambda} \phi$

to (3), and (in principle at least) this generates all other solutions via the principle of superposition.

Finite speed of propagation is a lot easier in the discrete setting, though one has to offset the support of the “velocity” field ${v}$ by one unit. Suppose we know that ${P}$ has unit speed in the sense that whenever ${f}$ is supported in a ball ${B(x,R)}$, then ${Pf}$ is supported in the ball ${B(x,R+1)}$. Then an easy induction shows that if ${u(0), v(0)}$ are supported in ${B(x_0,R), B(x_0,R+1)}$ respectively, then ${u(t), v(t)}$ are supported in ${B(x_0,R+t), B(x_0, R+t+1)}$.

The fundamental solution ${U(t) = U^t}$ to the discretised wave equation (3), in the sense of (2), is given by the formula

$\displaystyle U(t) = U^t = \begin{pmatrix} P & 1 \\ P^2-1 & P \end{pmatrix}^t$

$\displaystyle = \begin{pmatrix} T_t(P) & U_{t-1}(P) \\ (P^2-1) U_{t-1}(P) & T_t(P) \end{pmatrix}$

where ${T_t}$ and ${U_t}$ are the Chebyshev polynomials of the first and second kind, thus

$\displaystyle T_t( \cos \theta ) = \cos(t\theta)$

and

$\displaystyle U_t( \cos \theta ) = \frac{\sin((t+1)\theta)}{\sin \theta}.$

In particular, ${P}$ is now a minor of ${U(1) = U}$, and can also be viewed as an average of ${U}$ with its inverse ${U^{-1}}$:

$\displaystyle \begin{pmatrix} P & 0 \\ 0 & P \end{pmatrix} = \frac{1}{2} (U + U^{-1}). \ \ \ \ \ (5)$

As before, ${U}$ is unitary with respect to the energy form (4), so this is another instance of the dilation trick in action. The powers ${P^n}$ and ${U^n}$ are discrete analogues of the heat propagators ${e^{t\Delta/2}}$ and wave propagators ${U(t)}$ respectively.

One nice application of all this formalism, which I learned from Yuval Peres, is the Varopoulos-Carne inequality:

Theorem 1 (Varopoulos-Carne inequality) Let ${G}$ be a (possibly infinite) regular graph, let ${n \geq 1}$, and let ${x, y}$ be vertices in ${G}$. Then the probability that the simple random walk at ${x}$ lands at ${y}$ at time ${n}$ is at most ${2 \exp( - d(x,y)^2 / 2n )}$, where ${d}$ is the graph distance.

This general inequality is quite sharp, as one can see using the standard Cayley graph on the integers ${{\bf Z}}$. Very roughly speaking, it asserts that on a regular graph of reasonably controlled growth (e.g. polynomial growth), random walks of length ${n}$ concentrate on the ball of radius ${O(\sqrt{n})}$ or so centred at the origin of the random walk.

Proof: Let ${P \colon \ell^2(G) \rightarrow \ell^2(G)}$ be the graph Laplacian, thus

$\displaystyle Pf(x) = \frac{1}{D} \sum_{y \sim x} f(y)$

for any ${f \in \ell^2(G)}$, where ${D}$ is the degree of the regular graph and sum is over the ${D}$ vertices ${y}$ that are adjacent to ${x}$. This is a contraction of unit speed, and the probability that the random walk at ${x}$ lands at ${y}$ at time ${n}$ is

$\displaystyle \langle P^n \delta_x, \delta_y \rangle$

where ${\delta_x, \delta_y}$ are the Dirac deltas at ${x,y}$. Using (5), we can rewrite this as

$\displaystyle \langle (\frac{1}{2} (U + U^{-1}))^n \begin{pmatrix} 0 \\ \delta_x\end{pmatrix}, \begin{pmatrix} 0 \\ \delta_y\end{pmatrix} \rangle$

where we are now using the energy form (4). We can write

$\displaystyle (\frac{1}{2} (U + U^{-1}))^n = {\bf E} U^{S_n}$

where ${S_n}$ is the simple random walk of length ${n}$ on the integers, that is to say ${S_n = \xi_1 + \dots + \xi_n}$ where ${\xi_1,\dots,\xi_n = \pm 1}$ are independent uniform Bernoulli signs. Thus we wish to show that

$\displaystyle {\bf E} \langle U^{S_n} \begin{pmatrix} 0 \\ \delta_x\end{pmatrix}, \begin{pmatrix} 0 \\ \delta_y\end{pmatrix} \rangle \leq 2 \exp(-d(x,y)^2 / 2n ).$

By finite speed of propagation, the inner product here vanishes if ${|S_n| < d(x,y)}$. For ${|S_n| \geq d(x,y)}$ we can use Cauchy-Schwarz and the unitary nature of ${U}$ to bound the inner product by ${1}$. Thus the left-hand side may be upper bounded by

$\displaystyle {\bf P}( |S_n| \geq d(x,y) )$

and the claim now follows from the Chernoff inequality. $\Box$

This inequality has many applications, particularly with regards to relating the entropy, mixing time, and concentration of random walks with volume growth of balls; see this text of Lyons and Peres for some examples.

For sake of comparison, here is a continuous counterpart to the Varopoulos-Carne inequality:

Theorem 2 (Continuous Varopoulos-Carne inequality) Let ${t > 0}$, and let ${f,g \in L^2({\bf R}^d)}$ be supported on compact sets ${F,G}$ respectively. Then

$\displaystyle |\langle e^{t\Delta/2} f, g \rangle| \leq \sqrt{\frac{2t}{\pi d(F,G)^2}} \exp( - d(F,G)^2 / 2t ) \|f\|_{L^2} \|g\|_{L^2}$

where ${d(F,G)}$ is the Euclidean distance between ${F}$ and ${G}$.

Proof: By Fourier inversion one has

$\displaystyle e^{-t\xi^2/2} = \frac{1}{\sqrt{2\pi t}} \int_{\bf R} e^{-s^2/2t} e^{is\xi}\ ds$

$\displaystyle = \sqrt{\frac{2}{\pi t}} \int_0^\infty e^{-s^2/2t} \cos(s \xi )\ ds$

for any real ${\xi}$, and thus

$\displaystyle \langle e^{t\Delta/2} f, g\rangle = \sqrt{\frac{2}{\pi}} \int_0^\infty e^{-s^2/2t} \langle \cos(s \sqrt{-\Delta} ) f, g \rangle\ ds.$

By finite speed of propagation, the inner product ${\langle \cos(s \sqrt{-\Delta} ) f, g \rangle\ ds}$ vanishes when ${s < d(F,G)}$; otherwise, we can use Cauchy-Schwarz and the contractive nature of ${\cos(s \sqrt{-\Delta} )}$ to bound this inner product by ${\|f\|_{L^2} \|g\|_{L^2}}$. Thus

$\displaystyle |\langle e^{t\Delta/2} f, g\rangle| \leq \sqrt{\frac{2}{\pi t}} \|f\|_{L^2} \|g\|_{L^2} \int_{d(F,G)}^\infty e^{-s^2/2t}\ ds.$

Bounding ${e^{-s^2/2t}}$ by ${e^{-d(F,G)^2/2t} e^{-d(F,G) (s-d(F,G))/t}}$, we obtain the claim. $\Box$

Observe that the argument is quite general and can be applied for instance to other Riemannian manifolds than ${{\bf R}^d}$.

I’ve just uploaded to the arXiv my paper “The Elliott-Halberstam conjecture implies the Vinogradov least quadratic nonresidue conjecture“. As the title suggests, this paper links together the Elliott-Halberstam conjecture from sieve theory with the conjecture of Vinogradov concerning the least quadratic nonresidue ${n(p)}$ of a prime ${p}$. Vinogradov established the bound

$\displaystyle n(p) \ll p^{\frac{1}{2\sqrt{e}}} \log^2 p \ \ \ \ \ (1)$

and conjectured that

$\displaystyle n(p) \ll p^\varepsilon \ \ \ \ \ (2)$

for any fixed ${\varepsilon>0}$. Unconditionally, the best result so far (up to logarithmic factors) that holds for all primes ${p}$ is by Burgess, who showed that

$\displaystyle n(p) \ll p^{\frac{1}{4\sqrt{e}}+\varepsilon} \ \ \ \ \ (3)$

for any fixed ${\varepsilon>0}$. See this previous post for a proof of these bounds.

In this paper, we show that the Vinogradov conjecture is a consequence of the Elliott-Halberstam conjecture. Using a variant of the argument, we also show that the “Type II” estimates established by Zhang and numerically improved by the Polymath8a project can be used to improve a little on the Vinogradov bound (1), to

$\displaystyle n(p) \ll p^{(\frac{1}{2}-\frac{1}{68})\frac{1}{\sqrt{e}} + \varepsilon},$

although this falls well short of the Burgess bound. However, the method is somewhat different (although in both cases it is the Weil exponential sum bounds that are the source of the gain over (1)) and it is conceivable that a numerically stronger version of the Type II estimates could obtain results that are more competitive with the Burgess bound. At any rate, this demonstrates that the equidistribution estimates introduced by Zhang may have further applications beyond the family of results related to bounded gaps between primes.

The connection between the least quadratic nonresidue problem and Elliott-Halberstam is follows. Suppose for contradiction we can find a prime ${q}$ with ${n(q)}$ unusually large. Letting ${\chi}$ be the quadratic character modulo ${q}$, this implies that the sums ${\sum_{n \leq x} \chi(n)}$ are also unusually large for a significant range of ${x}$ (e.g. ${x < n(q)}$), although the sum is also quite small for large ${x}$ (e.g. ${x > q}$), due to the cancellation present in ${\chi}$. It turns out (by a sort of “uncertainty principle” for multiplicative functions, as per this paper of Granville and Soundararajan) that these facts force ${\sum_{n\leq x} \chi(n) \Lambda(n)}$ to be unusually large in magnitude for some large ${x}$ (with ${q^C \leq x \leq q^{C'}}$ for two large absolute constants ${C,C'}$). By the periodicity of ${\chi}$, this means that

$\displaystyle \sum_{n\leq x} \chi(n) \Lambda(n+q)$

must be unusually large also. However, because ${n(q)}$ is large, one can factorise ${\chi}$ as ${f * 1}$ for a fairly sparsely supported function ${f = \chi * \mu}$. The Elliott-Halberstam conjecture, which controls the distribution of ${\Lambda}$ in arithmetic progressions on the average can then be used to show that ${\sum_{n \leq x} (f*1)(n) \Lambda(n+q)}$ is small, giving the required contradiction.

The implication involving Type II estimates is proven by a variant of the argument. If ${n(q)}$ is large, then a character sum ${\sum_{N\leq n \leq 2N} \chi(n)}$ is unusually large for a certain ${N}$. By multiplicativity of ${\chi}$, this shows that ${\chi}$ correlates with ${\chi * 1_{[N,2N]}}$, and then by periodicity of ${\chi}$, this shows that ${\chi(n)}$ correlates with ${\chi * 1_{[N,2N]}(n+jq)}$ for various small ${q}$. By the Cauchy-Schwarz inequality (cf. this previous blog post), this implies that ${\chi * 1_{[N,2N]}(n+jq)}$ correlates with ${\chi * 1_{[N,2N]}(n+j'q)}$ for some distinct ${j,j'}$. But this can be ruled out by using Type II estimates.

I’ll record here a well-known observation concerning potential counterexamples to any improvement to the Burgess bound, that is to say an infinite sequence of primes ${p}$ with ${n(p) = p^{\frac{1}{4\sqrt{e}} + o(1)}}$. Suppose we let ${a(t)}$ be the asymptotic mean value of the quadratic character ${\chi}$ at ${p^t}$ and ${b(t)}$ the mean value of ${\chi \Lambda}$; these quantities are defined precisely in my paper, but roughly speaking one can think of

$\displaystyle a(t) = \lim_{p \rightarrow \infty} \frac{1}{p^t} \sum_{n \leq p^t} \chi(n)$

and

$\displaystyle b(t) = \lim_{p \rightarrow \infty} \frac{1}{p^t} \sum_{n \leq p^t} \chi(n) \Lambda(n).$

Thanks to the basic Dirichlet convolution identity ${\chi(n) \log(n) = \chi * \chi\Lambda(n)}$, one can establish the Wirsing integral equation

$\displaystyle t a(t) = \int_0^t a(u) b(t-u)\ du$

for all ${t \geq 0}$; see my paper for details (actually far sharper claims than this appear in previous work of Wirsing and Granville-Soundararajan). If we have an infinite sequence of counterexamples to any improvement to the Burgess bound, then we have

$\displaystyle a(t)=b(t) = 1 \hbox{ for } t < \frac{1}{4\sqrt{e}}$

while from the Burgess exponential sum estimates we have

$\displaystyle a(t) = 0 \hbox{ for } t > \frac{1}{4}.$

These two constraints, together with the Wirsing integral equation, end up determining ${a}$ and ${b}$ completely. It turns out that we must have

$\displaystyle b(t) = -1 \hbox{ for } \frac{1}{4\sqrt{e}} \leq t \leq \frac{1}{4}$

and

$\displaystyle a(t) = 1 - 2 \log(4 \sqrt{e} t) \hbox{ for } \frac{1}{4\sqrt{e}} \leq t \leq \frac{1}{4}$

and then for ${t > \frac{1}{4}}$, ${b}$ evolves by the integral equation

$\displaystyle b(t) = \int_{1/4\sqrt{e}}^{1/4} b(t-u) \frac{2du}{u}.$

For instance

$\displaystyle b(t) = 1 \hbox{ for } \frac{1}{4} < t \leq \frac{1}{2\sqrt{e}}$

and then ${b}$ oscillates in a somewhat strange fashion towards zero as ${t \rightarrow \infty}$. This very odd behaviour of ${\sum_n \chi(n) \Lambda(n)}$ is surely impossible, but it seems remarkably hard to exclude it without invoking a strong hypothesis, such as GRH or the Elliott-Halberstam conjecture (or weaker versions thereof).

The prime number theorem can be expressed as the assertion

$\displaystyle \sum_{n \leq x} \Lambda(n) = x + o(x) \ \ \ \ \ (1)$

as ${x \rightarrow \infty}$, where

$\displaystyle \Lambda(n) := \sum_{d|n} \mu(d) \log \frac{n}{d}$

is the von Mangoldt function. It is a basic result in analytic number theory, but requires a bit of effort to prove. One “elementary” proof of this theorem proceeds through the Selberg symmetry formula

$\displaystyle \sum_{n \leq x} \Lambda_2(n) = 2 x \log x + O(x) \ \ \ \ \ (2)$

where the second von Mangoldt function ${\Lambda_2}$ is defined by the formula

$\displaystyle \Lambda_2(n) := \sum_{d|n} \mu(d) \log^2 \frac{n}{d} \ \ \ \ \ (3)$

or equivalently

$\displaystyle \Lambda_2(n) = \Lambda(n) \log n + \sum_{d|n} \Lambda(d) \Lambda(\frac{n}{d}). \ \ \ \ \ (4)$

(We are avoiding the use of the ${*}$ symbol here to denote Dirichlet convolution, as we will need this symbol to denote ordinary convolution shortly.) For the convenience of the reader, we give a proof of the Selberg symmetry formula below the fold. Actually, for the purposes of proving the prime number theorem, the weaker estimate

$\displaystyle \sum_{n \leq x} \Lambda_2(n) = 2 x \log x + o(x \log x) \ \ \ \ \ (5)$

suffices.

In this post I would like to record a somewhat “soft analysis” reformulation of the elementary proof of the prime number theorem in terms of Banach algebras, and specifically in Banach algebra structures on (completions of) the space ${C_c({\bf R})}$ of compactly supported continuous functions ${f: {\bf R} \rightarrow {\bf C}}$ equipped with the convolution operation

$\displaystyle f*g(t) := \int_{\bf R} f(u) g(t-u)\ du.$

This soft argument does not easily give any quantitative decay rate in the prime number theorem, but by the same token it avoids many of the quantitative calculations in the traditional proofs of this theorem. Ultimately, the key “soft analysis” fact used is the spectral radius formula

$\displaystyle \lim_{n \rightarrow \infty} \|f^n\|^{1/n} = \sup_{\lambda \in \hat B} |\lambda(f)| \ \ \ \ \ (6)$

for any element ${f}$ of a unital commutative Banach algebra ${B}$, where ${\hat B}$ is the space of characters (i.e., continuous unital algebra homomorphisms from ${B}$ to ${{\bf C}}$) of ${B}$. This formula is due to Gelfand and may be found in any text on Banach algebras; for sake of completeness we prove it below the fold.

The connection between prime numbers and Banach algebras is given by the following consequence of the Selberg symmetry formula.

Theorem 1 (Construction of a Banach algebra norm) For any ${G \in C_c({\bf R})}$, let ${\|G\|}$ denote the quantity

$\displaystyle \|G\| := \limsup_{x \rightarrow \infty} |\sum_n \frac{\Lambda(n)}{n} G( \log \frac{x}{n} ) - \int_{\bf R} G(t)\ dt|.$

Then ${\| \|}$ is a seminorm on ${C_c({\bf R})}$ with the bound

$\displaystyle \|G\| \leq \|G\|_{L^1({\bf R})} := \int_{\bf R} |G(t)|\ dt \ \ \ \ \ (7)$

for all ${G \in C_c({\bf R})}$. Furthermore, we have the Banach algebra bound

$\displaystyle \| G * H \| \leq \|G\| \|H\| \ \ \ \ \ (8)$

for all ${G,H \in C_c({\bf R})}$.

We prove this theorem below the fold. The prime number theorem then follows from Theorem 1 and the following two assertions. The first is an application of the spectral radius formula (6) and some basic Fourier analysis (in particular, the observation that ${C_c({\bf R})}$ contains a plentiful supply of local units:

Theorem 2 (Non-trivial Banach algebras with many local units have non-trivial spectrum) Let ${\| \|}$ be a seminorm on ${C_c({\bf R})}$ obeying (7), (8). Suppose that ${\| \|}$ is not identically zero. Then there exists ${\xi \in {\bf R}}$ such that

$\displaystyle |\int_{\bf R} G(t) e^{-it\xi}\ dt| \leq \|G\|$

for all ${G \in C_c}$. In particular, by (7), one has

$\displaystyle \|G\| = \| G \|_{L^1({\bf R})}$

whenever ${G(t) e^{-it\xi}}$ is a non-negative function.

The second is a consequence of the Selberg symmetry formula and the fact that ${\Lambda}$ is real (as well as Mertens’ theorem, in the ${\xi=0}$ case), and is closely related to the non-vanishing of the Riemann zeta function ${\zeta}$ on the line ${\{ 1+i\xi: \xi \in {\bf R}\}}$:

Theorem 3 (Breaking the parity barrier) Let ${\xi \in {\bf R}}$. Then there exists ${G \in C_c({\bf R})}$ such that ${G(t) e^{-it\xi}}$ is non-negative, and

$\displaystyle \|G\| < \|G\|_{L^1({\bf R})}.$

Assuming Theorems 1, 2, 3, we may now quickly establish the prime number theorem as follows. Theorem 2 and Theorem 3 imply that the seminorm ${\| \|}$ constructed in Theorem 1 is trivial, and thus

$\displaystyle \sum_n \frac{\Lambda(n)}{n} G( \log \frac{x}{n} ) = \int_{\bf R} G(t)\ dt + o(1)$

as ${x \rightarrow \infty}$ for any Schwartz function ${G}$ (the decay rate in ${o(1)}$ may depend on ${G}$). Specialising to functions of the form ${G(t) = e^{-t} \eta( e^{-t} )}$ for some smooth compactly supported ${\eta}$ on ${(0,+\infty)}$, we conclude that

$\displaystyle \sum_n \Lambda(n) \eta(\frac{n}{x}) = \int_{\bf R} \eta(u)\ du + o(x)$

as ${x \rightarrow \infty}$; by the smooth Urysohn lemma this implies that

$\displaystyle \sum_{\varepsilon x \leq n \leq x} \Lambda(n) = x - \varepsilon x + o(x)$

as ${x \rightarrow \infty}$ for any fixed ${\varepsilon>0}$, and the prime number theorem then follows by a telescoping series argument.

The same argument also yields the prime number theorem in arithmetic progressions, or equivalently that

$\displaystyle \sum_{n \leq x} \Lambda(n) \chi(n) = o(x)$

for any fixed Dirichlet character ${\chi}$; the one difference is that the use of Mertens’ theorem is replaced by the basic fact that the quantity ${L(1,\chi) = \sum_n \frac{\chi(n)}{n}}$ is non-vanishing.

In graph theory, the recently developed theory of graph limits has proven to be a useful tool for analysing large dense graphs, being a convenient reformulation of the Szemerédi regularity lemma. Roughly speaking, the theory asserts that given any sequence ${G_n = (V_n, E_n)}$ of finite graphs, one can extract a subsequence ${G_{n_j} = (V_{n_j}, E_{n_j})}$ which converges (in a specific sense) to a continuous object known as a “graphon” – a symmetric measurable function ${p\colon [0,1] \times [0,1] \rightarrow [0,1]}$. What “converges” means in this context is that subgraph densities converge to the associated integrals of the graphon ${p}$. For instance, the edge density

$\displaystyle \frac{1}{|V_{n_j}|^2} |E_{n_j}|$

converge to the integral

$\displaystyle \int_0^1 \int_0^1 p(x,y)\ dx dy,$

the triangle density

$\displaystyle \frac{1}{|V_{n_j}|^3} \lvert \{ (v_1,v_2,v_3) \in V_{n_j}^3: \{v_1,v_2\}, \{v_2,v_3\}, \{v_3,v_1\} \in E_{n_j} \} \rvert$

converges to the integral

$\displaystyle \int_0^1 \int_0^1 \int_0^1 p(x_1,x_2) p(x_2,x_3) p(x_3,x_1)\ dx_1 dx_2 dx_3,$

the four-cycle density

$\displaystyle \frac{1}{|V_{n_j}|^4} \lvert \{ (v_1,v_2,v_3,v_4) \in V_{n_j}^4: \{v_1,v_2\}, \{v_2,v_3\}, \{v_3,v_4\}, \{v_4,v_1\} \in E_{n_j} \} \rvert$

converges to the integral

$\displaystyle \int_0^1 \int_0^1 \int_0^1 \int_0^1 p(x_1,x_2) p(x_2,x_3) p(x_3,x_4) p(x_4,x_1)\ dx_1 dx_2 dx_3 dx_4,$

and so forth. One can use graph limits to prove many results in graph theory that were traditionally proven using the regularity lemma, such as the triangle removal lemma, and can also reduce many asymptotic graph theory problems to continuous problems involving multilinear integrals (although the latter problems are not necessarily easy to solve!). See this text of Lovasz for a detailed study of graph limits and their applications.

One can also express graph limits (and more generally hypergraph limits) in the language of nonstandard analysis (or of ultraproducts); see for instance this paper of Elek and Szegedy, Section 6 of this previous blog post, or this paper of Towsner. (In this post we assume some familiarity with nonstandard analysis, as reviewed for instance in the previous blog post.) Here, one starts as before with a sequence ${G_n = (V_n,E_n)}$ of finite graphs, and then takes an ultraproduct (with respect to some arbitrarily chosen non-principal ultrafilter ${\alpha \in\beta {\bf N} \backslash {\bf N}}$) to obtain a nonstandard graph ${G_\alpha = (V_\alpha,E_\alpha)}$, where ${V_\alpha = \prod_{n\rightarrow \alpha} V_n}$ is the ultraproduct of the ${V_n}$, and similarly for the ${E_\alpha}$. The set ${E_\alpha}$ can then be viewed as a symmetric subset of ${V_\alpha \times V_\alpha}$ which is measurable with respect to the Loeb ${\sigma}$-algebra ${{\mathcal L}_{V_\alpha \times V_\alpha}}$ of the product ${V_\alpha \times V_\alpha}$ (see this previous blog post for the construction of Loeb measure). A crucial point is that this ${\sigma}$-algebra is larger than the product ${{\mathcal L}_{V_\alpha} \times {\mathcal L}_{V_\alpha}}$ of the Loeb ${\sigma}$-algebra of the individual vertex set ${V_\alpha}$. This leads to a decomposition

$\displaystyle 1_{E_\alpha} = p + e$

where the “graphon” ${p}$ is the orthogonal projection of ${1_{E_\alpha}}$ onto ${L^2( {\mathcal L}_{V_\alpha} \times {\mathcal L}_{V_\alpha} )}$, and the “regular error” ${e}$ is orthogonal to all product sets ${A \times B}$ for ${A, B \in {\mathcal L}_{V_\alpha}}$. The graphon ${p\colon V_\alpha \times V_\alpha \rightarrow [0,1]}$ then captures the statistics of the nonstandard graph ${G_\alpha}$, in exact analogy with the more traditional graph limits: for instance, the edge density

$\displaystyle \hbox{st} \frac{1}{|V_\alpha|^2} |E_\alpha|$

(or equivalently, the limit of the ${\frac{1}{|V_n|^2} |E_n|}$ along the ultrafilter ${\alpha}$) is equal to the integral

$\displaystyle \int_{V_\alpha} \int_{V_\alpha} p(x,y)\ d\mu_{V_\alpha}(x) d\mu_{V_\alpha}(y)$

where ${d\mu_V}$ denotes Loeb measure on a nonstandard finite set ${V}$; the triangle density

$\displaystyle \hbox{st} \frac{1}{|V_\alpha|^3} \lvert \{ (v_1,v_2,v_3) \in V_\alpha^3: \{v_1,v_2\}, \{v_2,v_3\}, \{v_3,v_1\} \in E_\alpha \} \rvert$

(or equivalently, the limit along ${\alpha}$ of the triangle densities of ${E_n}$) is equal to the integral

$\displaystyle \int_{V_\alpha} \int_{V_\alpha} \int_{V_\alpha} p(x_1,x_2) p(x_2,x_3) p(x_3,x_1)\ d\mu_{V_\alpha}(x_1) d\mu_{V_\alpha}(x_2) d\mu_{V_\alpha}(x_3),$

and so forth. Note that with this construction, the graphon ${p}$ is living on the Cartesian square of an abstract probability space ${V_\alpha}$, which is likely to be inseparable; but it is possible to cut down the Loeb ${\sigma}$-algebra on ${V_\alpha}$ to minimal countable ${\sigma}$-algebra for which ${p}$ remains measurable (up to null sets), and then one can identify ${V_\alpha}$ with ${[0,1]}$, bringing this construction of a graphon in line with the traditional notion of a graphon. (See Remark 5 of this previous blog post for more discussion of this point.)

Additive combinatorics, which studies things like the additive structure of finite subsets ${A}$ of an abelian group ${G = (G,+)}$, has many analogies and connections with asymptotic graph theory; in particular, there is the arithmetic regularity lemma of Green which is analogous to the graph regularity lemma of Szemerédi. (There is also a higher order arithmetic regularity lemma analogous to hypergraph regularity lemmas, but this is not the focus of the discussion here.) Given this, it is natural to suspect that there is a theory of “additive limits” for large additive sets of bounded doubling, analogous to the theory of graph limits for large dense graphs. The purpose of this post is to record a candidate for such an additive limit. This limit can be used as a substitute for the arithmetic regularity lemma in certain results in additive combinatorics, at least if one is willing to settle for qualitative results rather than quantitative ones; I give a few examples of this below the fold.

It seems that to allow for the most flexible and powerful manifestation of this theory, it is convenient to use the nonstandard formulation (among other things, it allows for full use of the transfer principle, whereas a more traditional limit formulation would only allow for a transfer of those quantities continuous with respect to the notion of convergence). Here, the analogue of a nonstandard graph is an ultra approximate group ${A_\alpha}$ in a nonstandard group ${G_\alpha = \prod_{n \rightarrow \alpha} G_n}$, defined as the ultraproduct of finite ${K}$-approximate groups ${A_n \subset G_n}$ for some standard ${K}$. (A ${K}$-approximate group ${A_n}$ is a symmetric set containing the origin such that ${A_n+A_n}$ can be covered by ${K}$ or fewer translates of ${A_n}$.) We then let ${O(A_\alpha)}$ be the external subgroup of ${G_\alpha}$ generated by ${A_\alpha}$; equivalently, ${A_\alpha}$ is the union of ${A_\alpha^m}$ over all standard ${m}$. This space has a Loeb measure ${\mu_{O(A_\alpha)}}$, defined by setting

$\displaystyle \mu_{O(A_\alpha)}(E_\alpha) := \hbox{st} \frac{|E_\alpha|}{|A_\alpha|}$

whenever ${E_\alpha}$ is an internal subset of ${A_\alpha^m}$ for any standard ${m}$, and extended to a countably additive measure; the arguments in Section 6 of this previous blog post can be easily modified to give a construction of this measure.

The Loeb measure ${\mu_{O(A_\alpha)}}$ is a translation invariant measure on ${O(A_{\alpha})}$, normalised so that ${A_\alpha}$ has Loeb measure one. As such, one should think of ${O(A_\alpha)}$ as being analogous to a locally compact abelian group equipped with a Haar measure. It should be noted though that ${O(A_\alpha)}$ is not actually a locally compact group with Haar measure, for two reasons:

• There is not an obvious topology on ${O(A_\alpha)}$ that makes it simultaneously locally compact, Hausdorff, and ${\sigma}$-compact. (One can get one or two out of three without difficulty, though.)
• The addition operation ${+\colon O(A_\alpha) \times O(A_\alpha) \rightarrow O(A_\alpha)}$ is not measurable from the product Loeb algebra ${{\mathcal L}_{O(A_\alpha)} \times {\mathcal L}_{O(A_\alpha)}}$ to ${{\mathcal L}_{O(\alpha)}}$. Instead, it is measurable from the coarser Loeb algebra ${{\mathcal L}_{O(A_\alpha) \times O(A_\alpha)}}$ to ${{\mathcal L}_{O(\alpha)}}$ (compare with the analogous situation for nonstandard graphs).

Nevertheless, the analogy is a useful guide for the arguments that follow.

Let ${L(O(A_\alpha))}$ denote the space of bounded Loeb measurable functions ${f\colon O(A_\alpha) \rightarrow {\bf C}}$ (modulo almost everywhere equivalence) that are supported on ${A_\alpha^m}$ for some standard ${m}$; this is a complex algebra with respect to pointwise multiplication. There is also a convolution operation ${\star\colon L(O(A_\alpha)) \times L(O(A_\alpha)) \rightarrow L(O(A_\alpha))}$, defined by setting

$\displaystyle \hbox{st} f \star \hbox{st} g(x) := \hbox{st} \frac{1}{|A_\alpha|} \sum_{y \in A_\alpha^m} f(y) g(x-y)$

whenever ${f\colon A_\alpha^m \rightarrow {}^* {\bf C}}$, ${g\colon A_\alpha^l \rightarrow {}^* {\bf C}}$ are bounded nonstandard functions (extended by zero to all of ${O(A_\alpha)}$), and then extending to arbitrary elements of ${L(O(A_\alpha))}$ by density. Equivalently, ${f \star g}$ is the pushforward of the ${{\mathcal L}_{O(A_\alpha) \times O(A_\alpha)}}$-measurable function ${(x,y) \mapsto f(x) g(y)}$ under the map ${(x,y) \mapsto x+y}$.

The basic structural theorem is then as follows.

Theorem 1 (Kronecker factor) Let ${A_\alpha}$ be an ultra approximate group. Then there exists a (standard) locally compact abelian group ${G}$ of the form

$\displaystyle G = {\bf R}^d \times {\bf Z}^m \times T$

for some standard ${d,m}$ and some compact abelian group ${T}$, equipped with a Haar measure ${\mu_G}$ and a measurable homomorphism ${\pi\colon O(A_\alpha) \rightarrow G}$ (using the Loeb ${\sigma}$-algebra on ${O(A_\alpha)}$ and the Borel ${\sigma}$-algebra on ${G}$), with the following properties:

• (i) ${\pi}$ has dense image, and ${\mu_G}$ is the pushforward of Loeb measure ${\mu_{O(A_\alpha)}}$ by ${\pi}$.
• (ii) There exists sets ${\{0\} \subset U_0 \subset K_0 \subset G}$ with ${U_0}$ open and ${K_0}$ compact, such that

$\displaystyle \pi^{-1}(U_0) \subset 4A_\alpha \subset \pi^{-1}(K_0). \ \ \ \ \ (1)$

• (iii) Whenever ${K \subset U \subset G}$ with ${K}$ compact and ${U}$ open, there exists a nonstandard finite set ${B}$ such that

$\displaystyle \pi^{-1}(K) \subset B \subset \pi^{-1}(U). \ \ \ \ \ (2)$

• (iv) If ${f, g \in L}$, then we have the convolution formula

$\displaystyle f \star g = \pi^*( (\pi_* f) \star (\pi_* g) ) \ \ \ \ \ (3)$

where ${\pi_* f,\pi_* g}$ are the pushforwards of ${f,g}$ to ${L^2(G, \mu_G)}$, the convolution ${\star}$ on the right-hand side is convolution using ${\mu_G}$, and ${\pi^*}$ is the pullback map from ${L^2(G,\mu_G)}$ to ${L^2(O(A_\alpha), \mu_{O(A_\alpha)})}$. In particular, if ${\pi_* f = 0}$, then ${f*g=0}$ for all ${g \in L}$.

One can view the locally compact abelian group ${G}$ as a “model “or “Kronecker factor” for the ultra approximate group ${A_\alpha}$ (in close analogy with the Kronecker factor from ergodic theory). In the case that ${A_\alpha}$ is a genuine nonstandard finite group rather than an ultra approximate group, the non-compact components ${{\bf R}^d \times {\bf Z}^m}$ of the Kronecker group ${G}$ are trivial, and this theorem was implicitly established by Szegedy. The compact group ${T}$ is quite large, and in particular is likely to be inseparable; but as with the case of graphons, when one is only studying at most countably many functions ${f}$, one can cut down the size of this group to be separable (or equivalently, second countable or metrisable) if desired, so one often works with a “reduced Kronecker factor” which is a quotient of the full Kronecker factor ${G}$.

Given any sequence of uniformly bounded functions ${f_n\colon A_n^m \rightarrow {\bf C}}$ for some fixed ${m}$, we can view the function ${f \in L}$ defined by

$\displaystyle f := \pi_* \hbox{st} \lim_{n \rightarrow \alpha} f_n \ \ \ \ \ (4)$

as an “additive limit” of the ${f_n}$, in much the same way that graphons ${p\colon V_\alpha \times V_\alpha \rightarrow [0,1]}$ are limits of the indicator functions ${1_{E_n}\colon V_n \times V_n \rightarrow \{0,1\}}$. The additive limits capture some of the statistics of the ${f_n}$, for instance the normalised means

$\displaystyle \frac{1}{|A_n|} \sum_{x \in A_n^m} f_n(x)$

converge (along the ultrafilter ${\alpha}$) to the mean

$\displaystyle \int_G f(x)\ d\mu_G(x),$

and for three sequences ${f_n,g_n,h_n\colon A_n^m \rightarrow {\bf C}}$ of functions, the normalised correlation

$\displaystyle \frac{1}{|A_n|^2} \sum_{x,y \in A_n^m} f_n(x) g_n(y) h_n(x+y)$

converges along ${\alpha}$ to the correlation

$\displaystyle \int_G \int_G f(x) g(y) h(x+y)\ d\mu_G(x) d\mu_G(y),$

the normalised ${U^2}$ Gowers norm

$\displaystyle ( \frac{1}{|A_n|^3} \sum_{x,y,z,w \in A_n^m: x+w=y+z} f_n(x) \overline{f_n(y)} \overline{f_n(z)} f_n(w))^{1/4}$

converges along ${\alpha}$ to the ${U^2}$ Gowers norm

$\displaystyle ( \int_{G \times G \times G} f(x) \overline{f(y)} \overline{f(z)} f_n(x+y-z)\ d\mu_G(x) d\mu_G(y) d\mu_G(z))^{1/4}$

and so forth. We caution however that some correlations that involve evaluating more than one function at the same point will not necessarily be preserved in the additive limit; for instance the normalised ${\ell^2}$ norm

$\displaystyle (\frac{1}{|A_n|} \sum_{x \in A_n^m} |f_n(x)|^2)^{1/2}$

does not necessarily converge to the ${L^2}$ norm

$\displaystyle (\int_G |f(x)|^2\ d\mu_G(x))^{1/2},$

but can converge instead to a larger quantity, due to the presence of the orthogonal projection ${\pi_*}$ in the definition (4) of ${f}$.

An important special case of an additive limit occurs when the functions ${f_n\colon A_n^m \rightarrow {\bf C}}$ involved are indicator functions ${f_n = 1_{E_n}}$ of some subsets ${E_n}$ of ${A_n^m}$. The additive limit ${f \in L}$ does not necessarily remain an indicator function, but instead takes values in ${[0,1]}$ (much as a graphon ${p}$ takes values in ${[0,1]}$ even though the original indicators ${1_{E_n}}$ take values in ${\{0,1\}}$). The convolution ${f \star f\colon G \rightarrow [0,1]}$ is then the ultralimit of the normalised convolutions ${\frac{1}{|A_n|} 1_{E_n} \star 1_{E_n}}$; in particular, the measure of the support of ${f \star f}$ provides a lower bound on the limiting normalised cardinality ${\frac{1}{|A_n|} |E_n + E_n|}$ of a sumset. In many situations this lower bound is an equality, but this is not necessarily the case, because the sumset ${2E_n = E_n + E_n}$ could contain a large number of elements which have very few (${o(|A_n|)}$) representations as the sum of two elements of ${E_n}$, and in the limit these portions of the sumset fall outside of the support of ${f \star f}$. (One can think of the support of ${f \star f}$ as describing the “essential” sumset of ${2E_n = E_n + E_n}$, discarding those elements that have only very few representations.) Similarly for higher convolutions of ${f}$. Thus one can use additive limits to partially control the growth ${k E_n}$ of iterated sumsets of subsets ${E_n}$ of approximate groups ${A_n}$, in the regime where ${k}$ stays bounded and ${n}$ goes to infinity.

Theorem 1 can be proven by Fourier-analytic means (combined with Freiman’s theorem from additive combinatorics), and we will do so below the fold. For now, we give some illustrative examples of additive limits.

Example 1 (Bohr sets) We take ${A_n}$ to be the intervals ${A_n := \{ x \in {\bf Z}: |x| \leq N_n \}}$, where ${N_n}$ is a sequence going to infinity; these are ${2}$-approximate groups for all ${n}$. Let ${\theta}$ be an irrational real number, let ${I}$ be an interval in ${{\bf R}/{\bf Z}}$, and for each natural number ${n}$ let ${B_n}$ be the Bohr set

$\displaystyle B_n := \{ x \in A^{(n)}: \theta x \hbox{ mod } 1 \in I \}.$

In this case, the (reduced) Kronecker factor ${G}$ can be taken to be the infinite cylinder ${{\bf R} \times {\bf R}/{\bf Z}}$ with the usual Lebesgue measure ${\mu_G}$. The additive limits of ${1_{A_n}}$ and ${1_{B_n}}$ end up being ${1_A}$ and ${1_B}$, where ${A}$ is the finite cylinder

$\displaystyle A := \{ (x,t) \in {\bf R} \times {\bf R}/{\bf Z}: x \in [-1,1]\}$

and ${B}$ is the rectangle

$\displaystyle B := \{ (x,t) \in {\bf R} \times {\bf R}/{\bf Z}: x \in [-1,1]; t \in I \}.$

Geometrically, one should think of ${A_n}$ and ${B_n}$ as being wrapped around the cylinder ${{\bf R} \times {\bf R}/{\bf Z}}$ via the homomorphism ${x \mapsto (\frac{x}{N_n}, \theta x \hbox{ mod } 1)}$, and then one sees that ${B_n}$ is converging in some normalised weak sense to ${B}$, and similarly for ${A_n}$ and ${A}$. In particular, the additive limit predicts the growth rate of the iterated sumsets ${kB_n}$ to be quadratic in ${k}$ until ${k|I|}$ becomes comparable to ${1}$, at which point the growth transitions to linear growth, in the regime where ${k}$ is bounded and ${n}$ is large.

If ${\theta = \frac{p}{q}}$ were rational instead of irrational, then one would need to replace ${{\bf R}/{\bf Z}}$ by the finite subgroup ${\frac{1}{q}{\bf Z}/{\bf Z}}$ here.

Example 2 (Structured subsets of progressions) We take ${A_n}$ be the rank two progression

$\displaystyle A_n := \{ a + b N_n^2: a,b \in {\bf Z}; |a|, |b| \leq N_n \},$

where ${N_n}$ is a sequence going to infinity; these are ${4}$-approximate groups for all ${n}$. Let ${B_n}$ be the subset

$\displaystyle B_n := \{ a + b N_n^2: a,b \in {\bf Z}; |a|^2 + |b|^2 \leq N_n^2 \}.$

Then the (reduced) Kronecker factor can be taken to be ${G = {\bf R}^2}$ with Lebesgue measure ${\mu_G}$, and the additive limits of the ${1_{A_n}}$ and ${1_{B_n}}$ are then ${1_A}$ and ${1_B}$, where ${A}$ is the square

$\displaystyle A := \{ (a,b) \in {\bf R}^2: |a|, |b| \leq 1 \}$

and ${B}$ is the circle

$\displaystyle B := \{ (a,b) \in {\bf R}^2: a^2+b^2 \leq 1 \}.$

Geometrically, the picture is similar to the Bohr set one, except now one uses a Freiman homomorphism ${a + b N_n^2 \mapsto (\frac{a}{N_n}, \frac{b}{N_n})}$ for ${a,b = O( N_n )}$ to embed the original sets ${A_n, B_n}$ into the plane ${{\bf R}^2}$. In particular, one now expects the growth rate of the iterated sumsets ${k A_n}$ and ${k B_n}$ to be quadratic in ${k}$, in the regime where ${k}$ is bounded and ${n}$ is large.

Example 3 (Dissociated sets) Let ${d}$ be a fixed natural number, and take

$\displaystyle A_n = \{0, v_1,\dots,v_d,-v_1,\dots,-v_d \}$

where ${v_1,\dots,v_d}$ are randomly chosen elements of a large cyclic group ${{\bf Z}/p_n{\bf Z}}$, where ${p_n}$ is a sequence of primes going to infinity. These are ${O(d)}$-approximate groups. The (reduced) Kronecker factor ${G}$ can (almost surely) then be taken to be ${{\bf Z}^d}$ with counting measure, and the additive limit of ${1_{A_n}}$ is ${1_A}$, where ${A = \{ 0, e_1,\dots,e_d,-e_1,\dots,-e_d\}}$ and ${e_1,\dots,e_d}$ is the standard basis of ${{\bf Z}^d}$. In particular, the growth rates of ${k A_n}$ should grow approximately like ${k^d}$ for ${k}$ bounded and ${n}$ large.

Example 4 (Random subsets of groups) Let ${A_n = G_n}$ be a sequence of finite additive groups whose order is going to infinity. Let ${B_n}$ be a random subset of ${G_n}$ of some fixed density ${0 \leq \lambda \leq 1}$. Then (almost surely) the Kronecker factor here can be reduced all the way to the trivial group ${\{0\}}$, and the additive limit of the ${1_{B_n}}$ is the constant function ${\lambda}$. The convolutions ${\frac{1}{|G_n|} 1_{B_n} * 1_{B_n}}$ then converge in the ultralimit (modulo almost everywhere equivalence) to the pullback of ${\lambda^2}$; this reflects the fact that ${(1-o(1))|G_n|}$ of the elements of ${G_n}$ can be represented as the sum of two elements of ${B_n}$ in ${(\lambda^2 + o(1)) |G_n|}$ ways. In particular, ${B_n+B_n}$ occupies a proportion ${1-o(1)}$ of ${G_n}$.

Example 5 (Trigonometric series) Take ${A_n = G_n = {\bf Z}/p_n {\bf C}}$ for a sequence ${p_n}$ of primes going to infinity, and for each ${n}$ let ${\xi_{n,1},\xi_{n,2},\dots}$ be an infinite sequence of frequencies chosen uniformly and independently from ${{\bf Z}/p_n{\bf Z}}$. Let ${f_n\colon {\bf Z}/p_n{\bf Z} \rightarrow {\bf C}}$ denote the random trigonometric series

$\displaystyle f_n(x) := \sum_{j=1}^\infty 2^{-j} e^{2\pi i \xi_{n,j} x / p_n }.$

Then (almost surely) we can take the reduced Kronecker factor ${G}$ to be the infinite torus ${({\bf R}/{\bf Z})^{\bf N}}$ (with the Haar probability measure ${\mu_G}$), and the additive limit of the ${f_n}$ then becomes the function ${f\colon ({\bf R}/{\bf Z})^{\bf N} \rightarrow {\bf R}}$ defined by the formula

$\displaystyle f( (x_j)_{j=1}^\infty ) := \sum_{j=1}^\infty e^{2\pi i x_j}.$

In fact, the pullback ${\pi^* f}$ is the ultralimit of the ${f_n}$. As such, for any standard exponent ${1 \leq q < \infty}$, the normalised ${l^q}$ norm

$\displaystyle (\frac{1}{p_n} \sum_{x \in {\bf Z}/p_n{\bf Z}} |f_n(x)|^q)^{1/q}$

can be seen to converge to the limit

$\displaystyle (\int_{({\bf R}/{\bf Z})^{\bf N}} |f(x)|^q\ d\mu_G(x))^{1/q}.$

The reader is invited to consider combinations of the above examples, e.g. random subsets of Bohr sets, to get a sense of the general case of Theorem 1.

It is likely that this theorem can be extended to the noncommutative setting, using the noncommutative Freiman theorem of Emmanuel Breuillard, Ben Green, and myself, but I have not attempted to do so here (see though this recent preprint of Anush Tserunyan for some related explorations); in a separate direction, there should be extensions that can control higher Gowers norms, in the spirit of the work of Szegedy.

Note: the arguments below will presume some familiarity with additive combinatorics and with nonstandard analysis, and will be a little sketchy in places.

One of the first basic theorems in group theory is Cayley’s theorem, which links abstract finite groups with concrete finite groups (otherwise known as permutation groups).

Theorem 1 (Cayley’s theorem) Let ${G}$ be a group of some finite order ${n}$. Then ${G}$ is isomorphic to a subgroup ${\tilde G}$ of the symmetric group ${S_n}$ on ${n}$ elements ${\{1,\dots,n\}}$. Furthermore, this subgroup is simply transitive: given two elements ${x,y}$ of ${\{1,\dots,n\}}$, there is precisely one element ${\sigma}$ of ${\tilde G}$ such that ${\sigma(x)=y}$.

One can therefore think of ${S_n}$ as a sort of “universal” group that contains (up to isomorphism) all the possible groups of order ${n}$.

Proof: The group ${G}$ acts on itself by multiplication on the left, thus each element ${g \in G}$ may be identified with a permutation ${\sigma_g: G \rightarrow G}$ on ${G}$ given by the map ${\sigma_g(h) := gh}$. This can be easily verified to identify ${G}$ with a simply transitive permutation group on ${G}$. The claim then follows by arbitrarily identifying ${G}$ with ${\{1,\dots,n\}}$. $\Box$

More explicitly, the permutation group ${\tilde G}$ arises by arbitrarily enumerating ${G}$ as ${\{s_1,\dots,s_n\}}$ and then associating to each group element ${g \in G}$ the permutation ${\sigma_g: \{1,\dots,n\} \rightarrow \{1,\dots,n\}}$ defined by the formula

$\displaystyle g s_i = s_{\sigma_g(i)}.$

The simply transitive group ${\tilde G}$ given by Cayley’s theorem is not unique, due to the arbitrary choice of identification of ${G}$ with ${\{1,\dots,n\}}$, but is unique up to conjugation by an element of ${S_n}$. On the other hand, it is easy to see that every simply transitive subgroup of ${S_n}$ is of order ${n}$, and that two such groups are isomorphic if and only if they are conjugate by an element of ${S_n}$. Thus Cayley’s theorem in fact identifies the moduli space of groups of order ${n}$ (up to isomorphism) with the simply transitive subgroups of ${S_n}$ (up to conjugacy by elements of ${S_n}$).

One can generalise Cayley’s theorem to groups of infinite order without much difficulty. But in this post, I would like to note an (easy) generalisation of Cayley’s theorem in a different direction, in which the group ${G}$ is no longer assumed to be of order ${n}$, but rather to have an index ${n}$ subgroup that is isomorphic to a fixed group ${H}$. The generalisation is:

Theorem 2 (Cayley’s theorem for ${H}$-sets) Let ${H}$ be a group, and let ${G}$ be a group that contains an index ${n}$ subgroup isomorphic to ${H}$. Then ${G}$ is isomorphic to a subgroup ${\tilde G}$ of the semidirect product ${S_n \ltimes H^n}$, defined explicitly as the set of tuples ${(\sigma, (h_i)_{i=1}^n)}$ with product

$\displaystyle (\sigma, (h_i)_{i=1}^n) (\rho, (k_i)_{i=1}^n) := (\sigma \circ \rho, (h_{\rho(i)} k_i)_{i=1}^n )$

and inverse

$\displaystyle (\sigma, (h_i)_{i=1}^n)^{-1} := (\sigma^{-1}, (h_{\sigma(i)}^{-1})_{i=1}^n).$

(This group is a wreath product of ${H}$ with ${S_n}$, and is sometimes denoted ${H \wr S_n}$, or more precisely ${H \wr_{\{1,\dots,n\}} S_n}$.) Furthermore, ${\tilde G}$ is simply transitive in the following sense: given any two elements ${x,y}$ of ${\{1,\dots,n\}}$ and ${h,k \in H}$, there is precisely one ${(\sigma, (h_i)_{i=1}^n)}$ in ${\tilde G}$ such that ${\sigma(x)=y}$ and ${k = h_x h}$.

Of course, Theorem 1 is the special case of Theorem 2 when ${H}$ is trivial. This theorem allows one to view ${S_n \ltimes H^n}$ as a “universal” group for modeling all groups containing a copy of ${H}$ as an index ${n}$ subgroup, in exactly the same way that ${S_n}$ is a universal group for modeling groups of order ${n}$. This observation is not at all deep, but I had not seen it before, so I thought I would record it here. (EDIT: as pointed out in comments, this is a slight variant of the universal embedding theorem of Krasner and Kaloujnine, which covers the case when ${H}$ is normal, in which case one can embed ${G}$ into the wreath product ${H \wr G/H}$, which is a subgroup of ${H \wr S_n}$.)

Proof: The basic idea here is to replace the category of sets in Theorem 1 by the category of ${H}$-sets, by which we mean sets ${X}$ with a right-action of the group ${H}$. A morphism between two ${H}$-sets ${X,Y}$ is a function ${f: X \rightarrow Y}$ which respects the right action of ${H}$, thus ${f(x)h = f(xh)}$ for all ${x \in X}$ and ${h \in H}$.

Observe that if ${G}$ contains a copy of ${H}$ as a subgroup, then one can view ${G}$ as an ${H}$-set, using the right-action of ${H}$ (which we identify with the indicated subgroup of ${G}$). The left action of ${G}$ on itself commutes with the right-action of ${H}$, and so we can represent ${G}$ by ${H}$-set automorphisms on the ${H}$-set ${G}$.

As ${H}$ has index ${n}$ in ${G}$, we see that ${G}$ is (non-canonically) isomorphic (as an ${H}$-set) to the ${H}$-set ${\{1,\dots,n\} \times H}$ with the obvious right action of ${H}$: ${(i,h) k := (i,hk)}$. It is easy to see that the group of ${H}$-set automorphisms of ${\{1,\dots,n\} \times H}$ can be identified with ${S^n \ltimes H}$, with the latter group acting on the former ${H}$-set by the rule

$\displaystyle (\sigma, (h_i)_{i=1}^n) (i,h) := (\sigma(i), h_i h)$

(it is routine to verify that this is indeed an action of ${S^n \ltimes H}$ by ${H}$-set automorphisms. It is then a routine matter to verify the claims (the simple transitivity of ${\tilde G}$ follows from the simple transitivity of the action of ${G}$ on itself). $\Box$

More explicitly, the group ${\tilde G}$ arises by arbitrarily enumerating the left-cosets of ${H}$ in ${G}$ as ${\{s_1H,\dots,s_nH\}}$ and then associating to each group element ${g \in G}$ the element ${(\sigma_g, (h_{g,i})_{i=1}^n )}$, where the permutation ${\sigma_g: \{1,\dots,n\} \rightarrow \{1,\dots,n\}}$ and the elements ${h_{g,i} \in H}$ are defined by the formula

$\displaystyle g s_i = s_{\sigma_g(i)} h_{g,i}.$

By noting that ${H^n}$ is an index ${n!}$ normal subgroup of ${S_n \ltimes H^n}$, we recover the classical result of Poincaré that any group ${G}$ that contains ${H}$ as an index ${n}$ subgroup, contains a normal subgroup ${N}$ of index dividing ${n!}$ that is contained in ${H}$. (Quotienting out the ${H}$ right-action, we recover also the classical proof of this result, as the action of ${G}$ on itself then collapses to the action of ${G}$ on the quotient space ${G/H}$, the stabiliser of which is ${N}$.)

Exercise 1 Show that a simply transitive subgroup ${\tilde G}$ of ${S_n \ltimes H^n}$ contains a copy of ${H}$ as an index ${n}$ subgroup; in particular, there is a canonical embedding of ${H}$ into ${\tilde G}$, and ${\tilde G}$ can be viewed as an ${H}$-set.

Exercise 2 Show that any two simply transitive subgroups ${\tilde G_1, \tilde G_2}$ of ${S_n \ltimes H^n}$ are isomorphic simultaneously as groups and as ${H}$-sets (that is, there is a bijection ${\phi: \tilde G_1 \rightarrow \tilde G_2}$ that is simultaneously a group isomorphism and an ${H}$-set isomorphism) if and only if they are conjugate by an element of ${S_n \times H_n}$.

The (presumably) final article arising from the Polymath8 project has now been uploaded to the arXiv as “The “bounded gaps between primes” Polymath project – a retrospective“.  This article, submitted to the Newsletter of the European Mathematical Society, consists of personal contributions from ten different participants (at varying levels of stage of career, and intensity of participation) on their own experiences with the project, and some thoughts as to what lessons to draw for any subsequent Polymath projects.  (At present, I do not know of any such projects being proposed, but from recent experience I would imagine that some opportunity suitable for a Polymath approach will present itself at some point in the near future.)

This post will also serve as the latest (and probably last) of the Polymath8 threads (rolling over this previous post), to wrap up any remaining discussion about any aspect of this project.

Analytic number theory is often concerned with the asymptotic behaviour of various arithmetic functions: functions ${f: {\bf N} \rightarrow {\bf R}}$ or ${f: {\bf N} \rightarrow {\bf C}}$ from the natural numbers ${{\bf N} = \{1,2,\dots\}}$ to the real numbers ${{\bf R}}$ or complex numbers ${{\bf C}}$. In this post, we will focus on the purely algebraic properties of these functions, and for reasons that will become clear later, it will be convenient to generalise the notion of an arithmetic function to functions ${f: {\bf N} \rightarrow R}$ taking values in some abstract commutative ring ${R}$. In this setting, we can add or multiply two arithmetic functions ${f,g: {\bf N} \rightarrow R}$ to obtain further arithmetic functions ${f+g, fg: {\bf N} \rightarrow R}$, and we can also form the Dirichlet convolution ${f*g: {\bf N} \rightarrow R}$ by the usual formula

$\displaystyle f*g(n) := \sum_{d|n} f(d) g(\frac{n}{d}).$

Regardless of what commutative ring ${R}$ is in used here, we observe that Dirichlet convolution is commutative, associative, and bilinear over ${R}$.

An important class of arithmetic functions in analytic number theory are the multiplicative functions, that is to say the arithmetic functions ${f: {\bf N} \rightarrow {\bf R}}$ such that ${f(1)=1}$ and

$\displaystyle f(nm) = f(n) f(m)$

for all coprime ${n,m \in {\bf N}}$. A subclass of these functions are the completely multiplicative functions, in which the restriction that ${n,m}$ be coprime is dropped. Basic examples of completely multiplicative functions (in the classical setting ${R={\bf C}}$) include

• the Kronecker delta ${\delta}$, defined by setting ${\delta(n)=1}$ for ${n=1}$ and ${\delta(n)=0}$ otherwise;
• the constant function ${1: n \mapsto 1}$ and the linear function ${n \mapsto n}$ (which by abuse of notation we denote by ${n}$);
• more generally monomials ${n \mapsto n^s}$ for any fixed complex number ${s}$ (in particular, the “Archimedean characters” ${n \mapsto n^{it}}$ for any fixed ${t \in {\bf R}}$), which by abuse of notation we denote by ${n^s}$;
• Dirichlet characters ${\chi}$;
• the Liouville function ${\lambda}$;
• the indicator function of the ${z}$-smooth numbers (numbers whose prime factors are all at most ${z}$), for some given ${z}$; and
• the indicator function of the ${z}$-rough numbers (numbers whose prime factors are all greater than ${z}$), for some given ${z}$.

Examples of multiplicative functions that are not completely multiplicative include

• the Möbius function ${\mu}$;
• the divisor function ${\tau}$ (also referred to as ${d}$);
• more generally, the higher order divisor functions ${\tau_k(n) = \sum_{d_1,\dots,d_k: d_1 \dots d_k = n} 1}$ for ${k \geq 1}$;
• the Euler totient function ${\phi}$;
• the number of roots ${n \mapsto \# \{ a \in {\bf Z}/n{\bf Z}: P(a) = 0\}}$ of a given polynomial ${P}$ defined over ${{\bf Z}}$;
• more generally, the point counting function ${n \mapsto V[{\bf Z}/n{\bf Z}]}$ of a given algebraic variety ${V}$ defined over ${{\bf Z}}$ (closely tied to the Hasse-Weil zeta function of ${V}$);
• the function ${r: n \mapsto r(n)}$ that counts the number of representations of ${n}$ as the sum of two squares;
• more generally, the function that maps a natural number ${n}$ to the number of ideals in a given number field ${K}$ of absolute norm ${n}$ (closely tied to the Dedekind zeta function of ${K}$).

These multiplicative functions interact well with the multiplication and convolution operations: if ${f,g: {\bf N} \rightarrow R}$ are multiplicative, then so are ${fg}$ and ${f * g}$, and if ${\psi}$ is completely multiplicative, then we also have

$\displaystyle \psi (f*g) = (\psi f) * (\psi g). \ \ \ \ \ (1)$

Finally, the product of completely multiplicative functions is again completely multiplicative. On the other hand, the sum of two multiplicative functions will never be multiplicative (just look at what happens at ${n=1}$), and the convolution of two completely multiplicative functions will usually just be multiplicative rather than completley multiplicative.

The specific multiplicative functions listed above are also related to each other by various important identities, for instance

$\displaystyle \delta * f = f; \quad \mu * 1 = \delta; \quad 1 * 1 = \tau; \quad \phi * 1 = n$

where ${f}$ is an arbitrary arithmetic function.

On the other hand, analytic number theory also is very interested in certain arithmetic functions that are not exactly multiplicative (and certainly not completely multiplicative). One particularly important such function is the von Mangoldt function ${\Lambda}$. This function is certainly not multiplicative, but is clearly closely related to such functions via such identities as ${\Lambda = \mu * L}$ and ${L = \Lambda * 1}$, where ${L: n\mapsto \log n}$ is the natural logarithm function. The purpose of this post is to point out that functions such as the von Mangoldt function lie in a class closely related to multiplicative functions, which I will call the derived multiplicative functions. More precisely:

Definition 1 A derived multiplicative function ${f: {\bf N} \rightarrow R}$ is an arithmetic function that can be expressed as the formal derivative

$\displaystyle f(n) = \frac{d}{d\varepsilon} F_\varepsilon(n) |_{\varepsilon=0}$

at the origin of a family ${f_\varepsilon: {\bf N}\rightarrow R}$ of multiplicative functions ${F_\varepsilon: {\bf N} \rightarrow R}$ parameterised by a formal parameter ${\varepsilon}$. Equivalently, ${f: {\bf N} \rightarrow R}$ is a derived multiplicative function if it is the ${\varepsilon}$ coefficient of a multiplicative function in the extension ${R[\varepsilon]/(\varepsilon^2)}$ of ${R}$ by a nilpotent infinitesimal ${\varepsilon}$; in other words, there exists an arithmetic function ${F: {\bf N} \rightarrow R}$ such that the arithmetic function ${F + \varepsilon f: {\bf N} \rightarrow R[\varepsilon]/(\varepsilon^2)}$ is multiplicative, or equivalently that ${F}$ is multiplicative and one has the Leibniz rule

$\displaystyle f(nm) = f(n) F(m) + F(n) f(m) \ \ \ \ \ (2)$

for all coprime ${n,m \in {\bf N}}$.

More generally, for any ${k\geq 0}$, a ${k}$-derived multiplicative function ${f: {\bf N} \rightarrow R}$ is an arithmetic function that can be expressed as the formal derivative

$\displaystyle f(n) = \frac{d^k}{d\varepsilon_1 \dots d\varepsilon_k} F_{\varepsilon_1,\dots,\varepsilon_k}(n) |_{\varepsilon_1,\dots,\varepsilon_k=0}$

at the origin of a family ${f_{\varepsilon_1,\dots,\varepsilon_k}: {\bf N} \rightarrow R}$ of multiplicative functions ${F_{\varepsilon_1,\dots,\varepsilon_k}: {\bf N} \rightarrow R}$ parameterised by formal parameters ${\varepsilon_1,\dots,\varepsilon_k}$. Equivalently, ${f}$ is the ${\varepsilon_1 \dots \varepsilon_k}$ coefficient of a multiplicative function in the extension ${R[\varepsilon_1,\dots,\varepsilon_k]/(\varepsilon_1^2,\dots,\varepsilon_k^2)}$ of ${R}$ by ${k}$ nilpotent infinitesimals ${\varepsilon_1,\dots,\varepsilon_k}$.

We define the notion of a ${k}$-derived completely multiplicative function similarly by replacing “multiplicative” with “completely multiplicative” in the above discussion.

There are Leibniz rules similar to (2) but they are harder to state; for instance, a doubly derived multiplicative function ${f: {\bf N} \rightarrow R}$ comes with singly derived multiplicative functions ${F_1, F_2: {\bf N} \rightarrow R}$ and a multiplicative function ${G: {\bf N} \rightarrow R}$ such that

$\displaystyle f(nm) = f(n) G(m) + F_1(n) F_2(m) + F_2(n) F_1(m) + G(n) f(m)$

for all coprime ${n,m \in {\bf N}}$.

One can then check that the von Mangoldt function ${\Lambda}$ is a derived multiplicative function, because ${\delta + \varepsilon \Lambda}$ is multiplicative in the ring ${{\bf C}[\varepsilon]/(\varepsilon^2)}$ with one infinitesimal ${\varepsilon}$. Similarly, the logarithm function ${L}$ is derived completely multiplicative because ${\exp( \varepsilon L ) := 1 + \varepsilon L}$ is completely multiplicative in ${{\bf C}[\varepsilon]/(\varepsilon^2)}$. More generally, any additive function ${\omega: {\bf N} \rightarrow R}$ is derived multiplicative because it is the top order coefficient of ${\exp(\varepsilon \omega) := 1 + \varepsilon \omega}$.

Remark 1 One can also phrase these concepts in terms of the formal Dirichlet series ${F(s) = \sum_n \frac{f(n)}{n^s}}$ associated to an arithmetic function ${f}$. A function ${f}$ is multiplicative if ${F}$ admits a (formal) Euler product; ${f}$ is derived multiplicative if ${F}$ is the (formal) first derivative of an Euler product with respect to some parameter (not necessarily ${s}$, although this is certainly an option); and so forth.

Using the definition of a ${k}$-derived multiplicative function as the top order coefficient of a multiplicative function of a ring with ${k}$ infinitesimals, it is easy to see that the product or convolution of a ${k}$-derived multiplicative function ${f: {\bf N} \rightarrow R}$ and a ${l}$-derived multiplicative function ${g: {\bf N} \rightarrow R}$ is necessarily a ${k+l}$-derived multiplicative function (again taking values in ${R}$). Thus, for instance, the higher-order von Mangoldt functions ${\Lambda_k := \mu * L^k}$ are ${k}$-derived multiplicative functions, because ${L^k}$ is a ${k}$-derived completely multiplicative function. More explicitly, ${L^k}$ is the top order coeffiicent of the completely multiplicative function ${\prod_{i=1}^k \exp(\varepsilon_i L)}$, and ${\Lambda_k}$ is the top order coefficient of the multiplicative function ${\mu * \prod_{i=1}^k \exp(\varepsilon_i L)}$, with both functions taking values in the ring ${C[\varepsilon_1,\dots,\varepsilon_k]/(\varepsilon_1^2,\dots,\varepsilon_k^2)}$ of complex numbers with ${k}$ infinitesimals ${\varepsilon_1,\dots,\varepsilon_k}$ attached.

It then turns out that most (if not all) of the basic identities used by analytic number theorists concerning derived multiplicative functions, can in fact be viewed as coefficients of identities involving purely multiplicative functions, with the latter identities being provable primarily from multiplicative identities, such as (1). This phenomenon is analogous to the one in linear algebra discussed in this previous blog post, in which many of the trace identities used there are derivatives of determinant identities. For instance, the Leibniz rule

$\displaystyle L (f * g) = (Lf)*g + f*(Lg)$

for any arithmetic functions ${f,g}$ can be viewed as the top order term in

$\displaystyle \exp(\varepsilon L) (f*g) = (\exp(\varepsilon L) f) * (\exp(\varepsilon L) g)$

in the ring with one infinitesimal ${\varepsilon}$, and then we see that the Leibniz rule is a special case (or a derivative) of (1), since ${\exp(\varepsilon L)}$ is completely multiplicative. Similarly, the formulae

$\displaystyle \Lambda = \mu * L; \quad L = \Lambda * 1$

are top order terms of

$\displaystyle (\delta + \varepsilon \Lambda) = \mu * \exp(\varepsilon L); \quad \exp(\varepsilon L) = (\delta + \varepsilon \Lambda) * 1,$

and the variant formula ${\Lambda = - (L\mu) * 1}$ is the top order term of

$\displaystyle (\delta + \varepsilon \Lambda) = (\exp(-\varepsilon L)\mu) * 1,$

which can then be deduced from the previous identities by noting that the completely multiplicative function ${\exp(-\varepsilon L)}$ inverts ${\exp(\varepsilon L)}$ multiplicatively, and also noting that ${L}$ annihilates ${\mu*1=\delta}$. The Selberg symmetry formula

$\displaystyle \Lambda_2 = \Lambda*\Lambda + \Lambda L, \ \ \ \ \ (3)$

which plays a key role in the Erdös-Selberg elementary proof of the prime number theorem (as discussed in this previous blog post), is the top order term of the identity

$\displaystyle \delta + \varepsilon_1 \Lambda + \varepsilon_2 \Lambda + \varepsilon_1\varepsilon_2 \Lambda_2 = (\exp(\varepsilon_2 L) (\delta + \varepsilon_1 \Lambda)) * (\delta + \varepsilon_2 \Lambda)$

involving the multiplicative functions ${\delta + \varepsilon_1 \Lambda + \varepsilon_2 \Lambda + \varepsilon_1\varepsilon_2 \Lambda_2}$, ${\exp(\varepsilon_2 L)}$, ${\delta+\varepsilon_1 \Lambda}$, ${\delta+\varepsilon_2 \Lambda}$ with two infinitesimals ${\varepsilon_1,\varepsilon_2}$, and this identity can be proven while staying purely within the realm of multiplicative functions, by using the identities

$\displaystyle \delta + \varepsilon_1 \Lambda + \varepsilon_2 \Lambda + \varepsilon_1\varepsilon_2 \Lambda_2 = \mu * (\exp(\varepsilon_1 L) \exp(\varepsilon_2 L))$

$\displaystyle \exp(\varepsilon_1 L) = 1 * (\delta + \varepsilon_1 \Lambda)$

$\displaystyle \delta + \varepsilon_2 \Lambda = \mu * \exp(\varepsilon_2 L)$

and (1). Similarly for higher identities such as

$\displaystyle \Lambda_3 = \Lambda L^2 + 3 \Lambda L * \Lambda + \Lambda * \Lambda * \Lambda$

which arise from expanding out ${\mu * (\exp(\varepsilon_1 L) \exp(\varepsilon_2 L) \exp(\varepsilon_3 L))}$ using (1) and the above identities; we leave this as an exercise to the interested reader.

An analogous phenomenon arises for identities that are not purely multiplicative in nature due to the presence of truncations, such as the Vaughan identity

$\displaystyle \Lambda_{> V} = \mu_{\leq U} * L - \mu_{\leq U} * \Lambda_{\leq V} * 1 + \mu_{>U} * \Lambda_{>V} * 1 \ \ \ \ \ (4)$

for any ${U,V \geq 1}$, where ${f_{>V} = f 1_{>V}}$ is the restriction of a multiplicative function ${f}$ to the natural numbers greater than ${V}$, and similarly for ${f_{\leq V}}$, ${f_{>U}}$, ${f_{\leq U}}$. In this particular case, (4) is the top order coefficient of the identity

$\displaystyle (\delta + \varepsilon \Lambda)_{>V} = \mu_{\leq U} * \exp(\varepsilon L) - \mu_{\leq U} * (\delta + \varepsilon \Lambda)_{\leq V} * 1$

$\displaystyle + \mu_{>U} * (\delta+\varepsilon \Lambda)_{>V} * 1$

which can be easily derived from the identities ${\delta = \mu_{\leq U} * 1 + \mu_{>U} * 1}$ and ${\exp(\varepsilon L) = (\delta + \varepsilon \Lambda)_{>V} * 1 + (\delta + \varepsilon \Lambda)_{\leq V} + 1}$. Similarly for the Heath-Brown identity

$\displaystyle \Lambda = \sum_{j=1}^K (-1)^{j-1} \binom{K}{j} \mu_{\leq U}^{*j} * 1^{*j-1} * L \ \ \ \ \ (5)$

valid for natural numbers up to ${U^K}$, where ${U \geq 1}$ and ${K \geq 1}$ are arbitrary parameters and ${f^{*j}}$ denotes the ${j}$-fold convolution of ${f}$, and discussed in this previous blog post; this is the top order coefficient of

$\displaystyle \delta + \varepsilon \Lambda = \sum_{j=1}^K (-1)^{j-1} \binom{K}{j} \mu_{\leq U}^{*j} * 1^{*j-1} * \exp( \varepsilon L )$

and arises by first observing that

$\displaystyle (\mu - \mu_{\leq U})^{*K} * 1^{*K-1} * \exp(\varepsilon L) = \mu_{>U}^{*K} * 1^{*K-1} * \exp( \varepsilon L )$

vanishes up to ${U^K}$, and then expanding the left-hand side using the binomial formula and the identity ${\mu^{*K} * 1^{*K-1} * \exp(\varepsilon L) = \delta + \varepsilon \Lambda}$.

One consequence of this phenomenon is that identities involving derived multiplicative functions tend to have a dimensional consistency property: all terms in the identity have the same order of derivation in them. For instance, all the terms in the Selberg symmetry formula (3) are doubly derived functions, all the terms in the Vaughan identity (4) or the Heath-Brown identity (5) are singly derived functions, and so forth. One can then use dimensional analysis to help ensure that one has written down a key identity involving such functions correctly, much as is done in physics.

In addition to the dimensional analysis arising from the order of derivation, there is another dimensional analysis coming from the value of multiplicative functions at primes ${p}$ (which is more or less equivalent to the order of pole of the Dirichlet series at ${s=1}$). Let us say that a multiplicative function ${f: {\bf N} \rightarrow R}$ has a pole of order ${j}$ if one has ${f(p)=j}$ on the average for primes ${p}$, where we will be a bit vague as to what “on the average” means as it usually does not matter in applications. Thus for instance, ${1}$ or ${\exp(\varepsilon L)}$ has a pole of order ${1}$ (a simple pole), ${\delta}$ or ${\delta + \varepsilon \Lambda}$ has a pole of order ${0}$ (i.e. neither a zero or a pole), Dirichlet characters also have a pole of order ${0}$ (although this is slightly nontrivial, requiring Dirichlet’s theorem), ${\mu}$ has a pole of order ${-1}$ (a simple zero), ${\tau}$ has a pole of order ${2}$, and so forth. Note that the convolution of a multiplicative function with a pole of order ${j}$ with a multiplicative function with a pole of order ${j'}$ will be a multiplicative function with a pole of order ${j+j'}$. If there is no oscillation in the primes ${p}$ (e.g. if ${f(p)=j}$ for all primes ${p}$, rather than on the average), it is also true that the product of a multiplicative function with a pole of order ${j}$ with a multiplicative function with a pole of order ${j'}$ will be a multiplicative function with a pole of order ${jj'}$. The situation is significantly different though in the presence of oscillation; for instance, if ${\chi}$ is a quadratic character then ${\chi^2}$ has a pole of order ${1}$ even though ${\chi}$ has a pole of order ${0}$.

A ${k}$-derived multiplicative function will then be said to have an underived pole of order ${j}$ if it is the top order coefficient of a multiplicative function with a pole of order ${j}$; in terms of Dirichlet series, this roughly means that the Dirichlet series has a pole of order ${j+k}$ at ${s=1}$. For instance, the singly derived multiplicative function ${\Lambda}$ has an underived pole of order ${0}$, because it is the top order coefficient of ${\delta + \varepsilon \Lambda}$, which has a pole of order ${0}$; similarly ${L}$ has an underived pole of order ${1}$, being the top order coefficient of ${\exp(\varepsilon L)}$. More generally, ${\Lambda_k}$ and ${L^k}$ have underived poles of order ${0}$ and ${1}$ respectively for any ${k}$.

By taking top order coefficients, we then see that the convolution of a ${k}$-derived multiplicative function with underived pole of order ${j}$ and a ${k'}$-derived multiplicative function with underived pole of order ${j'}$ is a ${k+k'}$-derived multiplicative function with underived pole of order ${j+j'}$. If there is no oscillation in the primes, the product of these functions will similarly have an underived pole of order ${jj'}$, for instance ${\Lambda L}$ has an underived pole of order ${0}$. We then have the dimensional consistency property that in any of the standard identities involving derived multiplicative functions, all terms not only have the same derived order, but also the same underived pole order. For instance, in (3), (4), (5) all terms have underived pole order ${0}$ (with any Mobius function terms being counterbalanced by a matching term of ${1}$ or ${L}$). This gives a second way to use dimensional analysis as a consistency check. For instance, any identity that involves a linear combination of ${\mu_{\leq U} * L}$ and ${\Lambda_{>V} * 1}$ is suspect because the underived pole orders do not match (being ${0}$ and ${1}$ respectively), even though the derived orders match (both are ${1}$).

One caveat, though: this latter dimensional consistency breaks down for identities that involve infinitely many terms, such as Linnik’s identity

$\displaystyle \Lambda = \sum_{i=0}^\infty (-1)^{i} L * 1_{>1}^{*i}.$

In this case, one can still rewrite things in terms of multiplicative functions as

$\displaystyle \delta + \varepsilon \Lambda = \sum_{i=0}^\infty (-1)^i \exp(\varepsilon L) * 1_{>1}^{*i},$

so the former dimensional consistency is still maintained.

I thank Andrew Granville, Kannan Soundararajan, and Emmanuel Kowalski for helpful conversations on these topics.

Tamar Ziegler and I have just uploaded to the arXiv our paper “Narrow progressions in the primes“, submitted to the special issue “Analytic Number Theory” in honor of the 60th birthday of Helmut Maier. The results here are vaguely reminiscent of the recent progress on bounded gaps in the primes, but use different methods.

About a decade ago, Ben Green and I showed that the primes contained arbitrarily long arithmetic progressions: given any ${k}$, one could find a progression ${n, n+r, \dots, n+(k-1)r}$ with ${r>0}$ consisting entirely of primes. In fact we showed the same statement was true if the primes were replaced by any subset of the primes of positive relative density.

A little while later, Tamar Ziegler and I obtained the following generalisation: given any ${k}$ and any polynomials ${P_1,\dots,P_k: {\bf Z} \rightarrow {\bf Z}}$ with ${P_1(0)=\dots=P_k(0)}$, one could find a “polynomial progression” ${n+P_1(r),\dots,n+P_k(r)}$ with ${r>0}$ consisting entirely of primes. Furthermore, we could make this progression somewhat “narrow” by taking ${r = n^{o(1)}}$ (where ${o(1)}$ denotes a quantity that goes to zero as ${n}$ goes to infinity). Again, the same statement also applies if the primes were replaced by a subset of positive relative density. My previous result with Ben corresponds to the linear case ${P_i(r) = (i-1)r}$.

In this paper we were able to make the progressions a bit narrower still: given any ${k}$ and any polynomials ${P_1,\dots,P_k: {\bf Z} \rightarrow {\bf Z}}$ with ${P_1(0)=\dots=P_k(0)}$, one could find a “polynomial progression” ${n+P_1(r),\dots,n+P_k(r)}$ with ${r>0}$ consisting entirely of primes, and such that ${r \leq \log^L n}$, where ${L}$ depends only on ${k}$ and ${P_1,\dots,P_k}$ (in fact it depends only on ${k}$ and the degrees of ${P_1,\dots,P_k}$). The result is still true if the primes are replaced by a subset of positive density ${\delta}$, but unfortunately in our arguments we must then let ${L}$ depend on ${\delta}$. However, in the linear case ${P_i(r) = (i-1)r}$, we were able to make ${L}$ independent of ${\delta}$ (although it is still somewhat large, of the order of ${k 2^k}$).

The polylogarithmic factor is somewhat necessary: using an upper bound sieve, one can easily construct a subset of the primes of density, say, ${90\%}$, whose arithmetic progressions ${n,n+r,\dots,n+(k-1)r}$ of length ${k}$ all obey the lower bound ${r \gg \log^{k-1} n}$. On the other hand, the prime tuples conjecture predicts that if one works with the actual primes rather than dense subsets of the primes, then one should have infinitely many length ${k}$ arithmetic progressions of bounded width for any fixed ${k}$. The ${k=2}$ case of this is precisely the celebrated theorem of Yitang Zhang that was the focus of the recently concluded Polymath8 project here. The higher ${k}$ case is conjecturally true, but appears to be out of reach of known methods. (Using the multidimensional Selberg sieve of Maynard, one can get ${m}$ primes inside an interval of length ${O( \exp(O(m)) )}$, but this is such a sparse set of primes that one would not expect to find even a progression of length three within such an interval.)

The argument in the previous paper was unable to obtain a polylogarithmic bound on the width of the progressions, due to the reliance on a certain technical “correlation condition” on a certain Selberg sieve weight ${\nu}$. This correlation condition required one to control arbitrarily long correlations of ${\nu}$, which was not compatible with a bounded value of ${L}$ (particularly if one wanted to keep ${L}$ independent of ${\delta}$).

However, thanks to recent advances in this area by Conlon, Fox, and Zhao (who introduced a very nice “densification” technique), it is now possible (in principle, at least) to delete this correlation condition from the arguments. Conlon-Fox-Zhao did this for my original theorem with Ben; and in the current paper we apply the densification method to our previous argument to similarly remove the correlation condition. This method does not fully eliminate the need to control arbitrarily long correlations, but allows most of the factors in such a long correlation to be bounded, rather than merely controlled by an unbounded weight such as ${\nu}$. This turns out to be significantly easier to control, although in the non-linear case we still unfortunately had to make ${L}$ large compared to ${\delta}$ due to a certain “clearing denominators” step arising from the complicated nature of the Gowers-type uniformity norms that we were using to control polynomial averages. We believe though that this an artefact of our method, and one should be able to prove our theorem with an ${L}$ that is uniform in ${\delta}$.

Here is a simple instance of the densification trick in action. Suppose that one wishes to establish an estimate of the form

$\displaystyle {\bf E}_n {\bf E}_r f(n) g(n+r) h(n+r^2) = o(1) \ \ \ \ \ (1)$

for some real-valued functions ${f,g,h}$ which are bounded in magnitude by a weight function ${\nu}$, but which are not expected to be bounded; this average will naturally arise when trying to locate the pattern ${(n,n+r,n+r^2)}$ in a set such as the primes. Here I will be vague as to exactly what range the parameters ${n,r}$ are being averaged over. Suppose that the factor ${g}$ (say) has enough uniformity that one can already show a smallness bound

$\displaystyle {\bf E}_n {\bf E}_r F(n) g(n+r) H(n+r^2) = o(1) \ \ \ \ \ (2)$

whenever ${F, H}$ are bounded functions. (One should think of ${F,H}$ as being like the indicator functions of “dense” sets, in contrast to ${f,h}$ which are like the normalised indicator functions of “sparse” sets). The bound (2) cannot be directly applied to control (1) because of the unbounded (or “sparse”) nature of ${f}$ and ${h}$. However one can “densify” ${f}$ and ${h}$ as follows. Since ${f}$ is bounded in magnitude by ${\nu}$, we can bound the left-hand side of (1) as

$\displaystyle {\bf E}_n \nu(n) | {\bf E}_r g(n+r) h(n+r^2) |.$

The weight function ${\nu}$ will be normalised so that ${{\bf E}_n \nu(n) = O(1)}$, so by the Cauchy-Schwarz inequality it suffices to show that

$\displaystyle {\bf E}_n \nu(n) | {\bf E}_r g(n+r) h(n+r^2) |^2 = o(1).$

The left-hand side expands as

$\displaystyle {\bf E}_n {\bf E}_r {\bf E}_s \nu(n) g(n+r) h(n+r^2) g(n+s) h(n+s^2).$

Now, it turns out that after an enormous (but finite) number of applications of the Cauchy-Schwarz inequality to steadily eliminate the ${g,h}$ factors, as well as a certain “polynomial forms condition” hypothesis on ${\nu}$, one can show that

$\displaystyle {\bf E}_n {\bf E}_r {\bf E}_s (\nu-1)(n) g(n+r) h(n+r^2) g(n+s) h(n+s^2) = o(1).$

(Because of the polynomial shifts, this requires a method known as “PET induction”, but let me skip over this point here.) In view of this estimate, we now just need to show that

$\displaystyle {\bf E}_n {\bf E}_r {\bf E}_s g(n+r) h(n+r^2) g(n+s) h(n+s^2) = o(1).$

Now we can reverse the previous steps. First, we collapse back to

$\displaystyle {\bf E}_n | {\bf E}_r g(n+r) h(n+r^2) |^2 = o(1).$

One can bound ${|{\bf E}_r g(n+r) h(n+r^2)|}$ by ${{\bf E}_r \nu(n+r) \nu(n+r^2)}$, which can be shown to be “bounded on average” in a suitable sense (e.g. bounded ${L^4}$ norm) via the aforementioned polynomial forms condition. Because of this and the Hölder inequality, the above estimate is equivalent to

$\displaystyle {\bf E}_n | {\bf E}_r g(n+r) h(n+r^2) | = o(1).$

By setting ${F}$ to be the signum of ${{\bf E}_r g(n+r) h(n+r^2)}$, this is equivalent to

$\displaystyle {\bf E}_n {\bf E}_r F(n) g(n+r) h(n+r^2) = o(1).$

This is halfway between (1) and (2); the sparsely supported function ${f}$ has been replaced by its “densification” ${F}$, but we have not yet densified ${h}$ to ${H}$. However, one can shift ${n}$ by ${r^2}$ and repeat the above arguments to achieve a similar densificiation of ${h}$, at which point one has reduced (1) to (2).

Kevin Ford, Ben Green, Sergei Konyagin, and myself have just posted to the arXiv our preprint “Large gaps between consecutive prime numbers“. This paper concerns the “opposite” problem to that considered by the recently concluded Polymath8 project, which was concerned with very small values of the prime gap ${p_{n+1}-p_n}$. Here, we wish to consider the largest prime gap ${G(X) = p_{n+1}-p_n}$ that one can find in the interval ${[X] = \{1,\dots,X\}}$ as ${X}$ goes to infinity.

Finding lower bounds on ${G(X)}$ is more or less equivalent to locating long strings of consecutive composite numbers that are not too large compared to the length of the string. A classic (and quite well known) construction here starts with the observation that for any natural number ${n}$, the consecutive numbers ${n!+2, n!+3,\dots,n!+n}$ are all composite, because each ${n!+i}$, ${i=2,\dots,n}$ is divisible by some prime ${p \leq n}$, while being strictly larger than that prime ${p}$. From this and Stirling’s formula, it is not difficult to obtain the bound

$\displaystyle G(X) \gg \frac{\log X}{\log\log X}. \ \ \ \ \ (1)$

A more efficient bound comes from the prime number theorem: there are only ${(1+o(1)) \frac{X}{\log X}}$ primes up to ${X}$, so just from the pigeonhole principle one can locate a string of consecutive composite numbers up to ${X}$ of length at least ${(1-o(1)) \log X}$, thus

$\displaystyle G(X) \gtrsim \log X \ \ \ \ \ (2)$

where we use ${X \gtrsim Y}$ or ${Y \lesssim X}$ as shorthand for ${X \geq (1-o(1)) Y}$ or ${Y \leq (1+o(1)) X}$.

What about upper bounds? The Cramér random model predicts that the primes up to ${X}$ are distributed like a random subset ${\{1,\dots,X\}}$ of density ${1/\log X}$. Using this model, Cramér arrived at the conjecture

$\displaystyle G(X) \ll \log^2 X.$

In fact, if one makes the extremely optimistic assumption that the random model perfectly describes the behaviour of the primes, one would arrive at the even more precise prediction

$\displaystyle G(X) \sim \log^2 X.$

However, it is no longer widely believed that this optimistic version of the conjecture is true, due to some additional irregularities in the primes coming from the basic fact that large primes cannot be divisible by very small primes. Using the Maier matrix method to capture some of this irregularity, Granville was led to the conjecture that

$\displaystyle G(X) \gtrsim 2e^{-\gamma} \log^2 X$

(note that ${2e^{-\gamma} = 1.1229\dots}$ is slightly larger than ${1}$). For comparison, the known upper bounds on ${G(X)}$ are quite weak; unconditionally one has ${G(X) \ll X^{0.525}}$ by the work of Baker, Harman, and Pintz, and even on the Riemann hypothesis one only gets down to ${G(X) \ll X^{1/2} \log X}$, as shown by Cramér (a slight improvement is also possible if one additionally assumes the pair correlation conjecture; see this article of Heath-Brown and the references therein).

This conjecture remains out of reach of current methods. In 1931, Westzynthius managed to improve the bound (2) slightly to

$\displaystyle G(X) \gg \frac{\log\log\log X}{\log\log\log\log X} \log X ,$

which Erdös in 1935 improved to

$\displaystyle G(X) \gg \frac{\log\log X}{(\log\log\log X)^2} \log X$

and Rankin in 1938 improved slightly further to

$\displaystyle G(X) \gtrsim c \frac{\log\log X (\log\log\log\log X)}{(\log\log\log X)^2} \log X \ \ \ \ \ (3)$

with ${c=1/3}$. Remarkably, this rather strange bound then proved extremely difficult to advance further on; until recently, the only improvements were to the constant ${c}$, which was raised to ${c=\frac{1}{2} e^\gamma}$ in 1963 by Schönhage, to ${c= e^\gamma}$ in 1963 by Rankin, to ${c = 1.31256 e^\gamma}$ by Maier and Pomerance, and finally to ${c = 2e^\gamma}$ in 1997 by Pintz.

Erdös listed the problem of making ${c}$ arbitrarily large one of his favourite open problems, even offering (“somewhat rashly”, in his words) a cash prize for the solution. Our main result answers this question in the affirmative:

Theorem 1 The bound (3) holds for arbitrarily large ${c>0}$.

In principle, we thus have a bound of the form

$\displaystyle G(X) \geq f(X) \frac{\log\log X (\log\log\log\log X)}{(\log\log\log X)^2} \log X$

for some ${f(X)}$ that grows to infinity. Unfortunately, due to various sources of ineffectivity in our methods, we cannot provide any explicit rate of growth on ${f(X)}$ at all.

We decided to announce this result the old-fashioned way, as part of a research lecture; more precisely, Ben Green announced the result in his ICM lecture this Tuesday. (The ICM staff have very efficiently put up video of his talks (and most of the other plenary and prize talks) online; Ben’s talk is here, with the announcement beginning at about 0:48. Note a slight typo in his slides, in that the exponent of ${\log\log\log X}$ in the denominator is ${3}$ instead of ${2}$.) Ben’s lecture slides may be found here.

By coincidence, an independent proof of this theorem has also been obtained very recently by James Maynard.

I discuss our proof method below the fold.

[This guest post is authored by Matilde Lalin, an Associate Professor in the Département de mathématiques et de statistique at the Université de Montréal.  I have lightly edited the text, mostly by adding some HTML formatting. -T.]

Mathematicians (and likely other academics!) with small children face some unique challenges when traveling to conferences and workshops. The goal of this post is to reflect on these, and to start a constructive discussion what institutions and event organizers could do to improve the experiences of such participants.

The first necessary step is to recognize that different families have different needs. While it is hard to completely address everybody’s needs, there are some general measures that have a good chance to help most of the people traveling with young children. In this post, I will mostly focus on nursing mothers with infants ($\leq 24$ months old) because that is my personal experience. Many of the suggestions will apply to other cases such as non-nursing babies, children of single parents, children of couples of mathematicians who are interested in attending the same conference, etc..

The mother of a nursing infant that wishes to attend a conference has three options:

1. Bring the infant and a relative/friend to help caring for the infant. The main challenge in this case is to fund the trip expenses of the relative. This involves trip costs, lodging, and food. The family may need a hotel room with some special amenities such as crib, fridge, microwave, etc. Location is also important, with easy access to facilities such as a grocery store, pharmacy, etc. The mother will need to take regular breaks from the conference in order to nurse the baby (this could be as often as every three hours or so). Depending on personal preferences, she may need to nurse privately. It is convenient, thus, to make a private room available, located as close to the conference venue as possible. The relative may need to have a place to stay with the baby near the conference such as a playground or a room with toys, particularly if the hotel room is far.
2. Bring the infant and hire someone local (a nanny) to help caring for the infant. The main challenges in this case are two: finding the caregiver and paying for such services. Finding a caregiver in a place where one does not live is hard, as it is difficult to conduct interviews or get references. There are agencies that can do this for a (quite expensive) fee: they will find a professional caregiver with background checks, CPR certification, many references, etc. It may be worth it, though, as professional caregivers tend to provide high-quality services and peace of mind is priceless for the mother mathematician attending a conference. As in the previous case, the mother may have particular needs regarding the hotel room, location, and all the other facilities mentioned for Option 1.
3. Travel without the infant and pump milk regularly. This can be very challenging for the mother, the baby, and the person that stays behind taking care of the baby, but the costs of this arrangement are much lower than in Option 1 or 2 (I am ignoring the possibility that the family needs to hire help at home, which is necessary in some cases). A nursing mother away from her baby has no option but to pump her milk to prevent her from pain and serious health complications. This mother may have to pump milk very often. Pumping is less efficient than nursing, so she will be gone for longer in each break or she will have more breaks compared to a mother that travels with her baby. For pumping, people need a room which should ideally be private, with a sink, and located as close to the conference venue as possible. It is often impossible for these three conditions to be met at the same time, so different mothers give priority to different features. Some people pump milk in washrooms, to have easy access to water. Other people might prefer to pump in a more comfortable setting, such as an office, and go to the washroom to wash the breast pump accessories after. If the mother expects that the baby will drink breastmilk while she is away, then she will also have to pump milk in advance of her trip. This requires some careful planning.

Many pumping mothers try to store the pumped milk and bring it back home. In this case the mother needs a hotel room with a fridge which (ideally, but hard to find) has a freezer. In a perfect world there would also be a fridge in the place where she pumps/where the conference is held.

It is important to keep in mind that each option has its own set of challenges (even when expenses and facilities are all covered) and that different families may be restricted in their choice of options for a variety of reasons. It is therefore important that all these three options be facilitated.

As for the effect these choices have on the conference experience for the mother, Option 1 means that she has to balance her time between the conference and spending time with her relative/friend. This pressure disappears when we consider Option 2, so this option may lead to more participation in the conferences activities. In Option 3, the mother is in principle free to participate in all the conference activities, but the frequent breaks may limit the type of activity. A mother may choose different options depending on the nature of the conference.

I want to stress, for the three options, that having to make choices about what to miss in the conference is very hard. While talks are important, so are the opportunities to meet people and discuss mathematics that happen during breaks and social events. It is very difficult to balance all of this. This is particularly difficult for the pumping mother in Option 3: because she travels without her baby, she is not perceived to be a in special situation or in need of accommodation. However, this mother is probably choosing between going to the last lecture in the morning or having lunch alone, because if she goes to pump right after the last lecture, by the time she is back, everybody has left for lunch.

Here is the Hall of Fame for those organizations that are already supporting nursing mothers’ travels in mathematics:

• The Natural Sciences and Engineering Research Council of Canada (NSERC) (search for “child care”) allows to reimburse the costs of child care with Option 2 out of the mother’s grants. They will also reimburse the travel expenses of a relative with Option 1 up to the amount that would cost to hire a local caregiver.
• The ENFANT/ELEFANT conference (co-organized by Lillian Pierce and Damaris Schindler) provided a good model to follow regarding accommodation for parents with children during conferences that included funding for covering the travel costs of accompanying caretakers (the funding was provided by the Deutsche Forschungsgemeinschaft, and lactation rooms and play rooms near the conference venue (the facilities were provided by the Hausdorff Center for Mathematics).

Additional information (where to go with kids, etc) was provided on site by the organizers and was made available to all participants all the time, by means of a display board that was left standing during the whole week of the conference.
• The American Institute of Mathematics (AIM) reimburses up to 500 dollars on childcare for visitors and they have some online resources that assist in finding childcare and nannies.

[UPDATED] Added a few more things to the Hall of Fame

In closing, here is a (possibly incomplete) list of resources that institutes, funding agencies, and conferences could consider providing for nursing mother mathematicians:

1. Funding (for cost associated to child care either professional or by an accompanying relative).
2. List of childcare resources (nannies, nanny agencies, drop-in childcare centre, etc).
3. Nursing rooms and playrooms near the conference venue. Nearby fridge.
4. Breaks of at least 20 minutes every 2-3 hours.
5. Information about transportation with infants. More specific, taxi and/or shuttle companies that provide infant car seats. Information regarding the law on infant seats in taxis and other public transportation.
6. Accessibility for strollers.
7. [UPDATED] A nearby playground location. (comment from Peter).

I also find it important that these resources be listed publicly in the institute/conference website. This serves a double purpose: first, it helps those in need of the resources to access them easily, and second, it contributes to make these accommodations normal, setting a good model for future events, and inspiring organizers of future events.

Finally, I am pretty sure that the options and solutions I described do not cover all cases. I would like to finish this note by inviting readers to make suggestions, share experiences, and/or pose questions about this topic.