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A sequence {a: {\bf Z} \rightarrow {\bf C}} of complex numbers is said to be quasiperiodic if it is of the form

\displaystyle  a(n) = F( \alpha_1 n \hbox{ mod } 1, \dots, \alpha_k n \hbox{ mod } 1)

for some real numbers {\alpha_1,\dots,\alpha_k} and continuous function {F: ({\bf R}/{\bf Z})^k \rightarrow {\bf C}}. For instance, linear phases such as {n \mapsto e(\alpha n + \beta)} (where {e(\theta) := e^{2\pi i \theta}}) are examples of quasiperiodic sequences; the top order coefficient {\alpha} (modulo {1}) can be viewed as a “frequency” of the integers, and an element of the Pontryagin dual {\hat {\bf Z} \equiv {\bf R}/{\bf Z}} of the integers. Any periodic sequence is also quasiperiodic (taking {k=1} and {\alpha_1} to be the reciprocal of the period). A sequence is said to be almost periodic if it is the uniform limit of quasiperiodic sequences. For instance any Fourier series of the form

\displaystyle  a(n) = \sum_{j=1}^\infty c_j e(\alpha_j n)

with {\alpha_1,\alpha_2,\dots} real numbers and {c_1,c_2,\dots} an absolutely summable sequence of complex coefficients, will be almost periodic.

These sequences arise in various “complexity one” problems in arithmetic combinatorics and ergodic theory. For instance, if {(X, \mu, T)} is a measure-preserving system – a probability space {(X,\mu)} equipped with a measure-preserving shift, and {f_1,f_2 \in L^\infty(X)} are bounded measurable functions, then the correlation sequence

\displaystyle  a(n) := \int_X f_1(x) f_2(T^n x)\ d\mu(x)

can be shown to be an almost periodic sequence, plus an error term {b_n} which is “null” in the sense that it has vanishing uniform density:

\displaystyle  \sup_N \frac{1}{M} \sum_{n=N+1}^{n+M} |b_n| \rightarrow 0 \hbox{ as } M \rightarrow \infty.

This can be established in a number of ways, for instance by writing {a(n)} as the Fourier coefficients of the spectral measure of the shift {T} with respect to the functions {f_1,f_2}, and then decomposing that measure into pure point and continuous components.

In the last two decades or so, it has become clear that there are natural higher order versions of these concepts, in which linear polynomials such as {\alpha n + \beta} are replaced with higher degree counterparts. The most obvious candidates for these counterparts would be the polynomials {\alpha_d n^d + \dots + \alpha_0}, but this turns out to not be a complete set of higher degree objects needed for the theory. Instead, the higher order versions of quasiperiodic and almost periodic sequences are now known as basic nilsequences and nilsequences respectively, while the higher order version of a linear phase is a nilcharacter; each nilcharacter then has a symbol that is a higher order generalisation of the concept of a frequency (and the collection of all symbols forms a group that can be viewed as a higher order version of the Pontryagin dual of {{\bf Z}}). The theory of these objects is spread out in the literature across a number of papers; in particular, the theory of nilcharacters is mostly developed in Appendix E of this 116-page paper of Ben Green, Tamar Ziegler, and myself, and is furthermore written using nonstandard analysis and treating the more general setting of higher dimensional sequences. I therefore decided to rewrite some of that material in this blog post, in the simpler context of the qualitative asymptotic theory of one-dimensional nilsequences and nilcharacters rather than the quantitative single-scale theory that is needed for combinatorial applications (and which necessitated the use of nonstandard analysis in the previous paper).

For technical reasons (having to do with the non-trivial topological structure on nilmanifolds), it will be convenient to work with vector-valued sequences, that take values in a finite-dimensional complex vector space {{\bf C}^m} rather than in {{\bf C}}. By doing the space of sequences is now, technically, no longer a ring, as the operations of addition and multiplication on vector-valued sequences become ill-defined. However, we can still take complex conjugates of a sequence, and add sequences taking values in the same vector space {{\bf C}^m}, and for sequences taking values in different vector spaces {{\bf C}^m}, {{\bf C}^{m'}}, we may utilise the tensor product {\otimes: {\bf C}^m \times {\bf C}^{m'} \rightarrow {\bf C}^{mm'}}, which we will normalise by defining

\displaystyle  (z_1,\dots,z_m) \otimes (w_1,\dots,w_{m'}) = (z_1 w_1, \dots, z_1 w_{m'}, \dots, z_m w_1, \dots, z_m w_{m'} ).

This product is associative and bilinear, and also commutative up to permutation of the indices. It also interacts well with the Hermitian norm

\displaystyle  \| (z_1,\dots,z_m) \| := \sqrt{|z_1|^2 + \dots + |z_m|^2}

since we have {\|z \otimes w\| = \|z\| \|w\|}.

The traditional definition of a basic nilsequence (as defined for instance by Bergelson, Host, and Kra) is as follows:

Definition 1 (Basic nilsequence, first definition) A nilmanifold of step at most {d} is a quotient {G/\Gamma}, where {G} is a connected, simply connected nilpotent Lie group of step at most {d} (thus, all {d+1}-fold commutators vanish) and {\Gamma} is a discrete cocompact lattice in {G}. A basic nilsequence of degree at most {d} is a sequence of the form {n \mapsto F(g^n g_0 \Gamma)}, where {g_0 \Gamma \in G/\Gamma}, {g \in G}, and {F: G/\Gamma \rightarrow {\bf C}^m} is a continuous function.

For instance, it is not difficult using this definition to show that a sequence is a basic nilsequence of degree at most {1} if and only if it is quasiperiodic. The requirement that {G} be simply connected can be easily removed if desired by passing to a universal cover, but it is technically convenient to assume it (among other things, it allows for a well-defined logarithm map that obeys the Baker-Campbell-Hausdorff formula). When one wishes to perform a more quantitative analysis of nilsequences (particularly when working on a “single scale”. sich as on a single long interval {\{ N+1, \dots, N+M\}}), it is common to impose additional regularity conditions on the function {F}, such as Lipschitz continuity or smoothness, but ordinary continuity will suffice for the qualitative discussion in this blog post.

Nowadays, and particularly when one needs to understand the “single-scale” equidistribution properties of nilsequences, it is more convenient (as is for instance done in this ICM paper of Green) to use an alternate definition of a nilsequence as follows.

Definition 2 Let {d \geq 0}. A filtered group of degree at most {d} is a group {G} together with a sequence {G_\bullet = (G_0,G_1,G_2,\dots)} of subgroups {G \geq G_0 \geq G_1 \geq \dots} with {G_{d+1}=\{\hbox{id}\}} and {[G_i,G_j] \subset G_{i+j}} for {i,j \geq 0}. A polynomial sequence {g: {\bf Z} \rightarrow G} into a filtered group is a function such that {\partial_{h_i} \dots \partial_{h_1} g(n) \in G_i} for all {i \geq 0} and {n,h_1,\dots,h_i \in{\bf Z}}, where {\partial_h g(n) := g(n+h) g(n)^{-1}} is the difference operator. A filtered nilmanifold of degree at most {s} is a quotient {G/\Gamma}, where {G} is a filtered group of degree at most {s} such that {G} and all of the subgroups {G_i} are connected, simply connected nilpotent filtered Lie group, and {\Gamma} is a discrete cocompact subgroup of {G} such that {\Gamma_i := \Gamma \cap G_i} is a discrete cocompact subgroup of {G_i}. A basic nilsequence of degree at most {d} is a sequence of the form {n \mapsto F(g(n))}, where {g: {\bf Z} \rightarrow G} is a polynomial sequence, {G/\Gamma} is a filtered nilmanifold of degree at most {d}, and {F: G \rightarrow {\bf C}^m} is a continuous function which is {\Gamma}-automorphic, in the sense that {F(g \gamma) = F(g)} for all {g \in G} and {\gamma \in \Gamma}.

One can easily identify a {\Gamma}-automorphic function on {G} with a function on {G/\Gamma}, but there are some (very minor) advantages to working on the group {G} instead of the quotient {G/\Gamma}, as it becomes slightly easier to modify the automorphy group {\Gamma} when needed. (But because the action of {\Gamma} on {G} is free, one can pass from {\Gamma}-automorphic functions on {G} to functions on {G/\Gamma} with very little difficulty.) The main reason to work with polynomial sequences {n \mapsto g(n)} rather than geometric progressions {n \mapsto g^n g_0 \Gamma} is that they form a group, a fact essentially established by by Lazard and Leibman; see Corollary B.4 of this paper of Green, Ziegler, and myself for a proof in the filtered group setting.

It is easy to see that any sequence that is a basic nilsequence of degree at most {d} in the sense of the first definition, is also a basic nilsequence of degree at most {d} in the second definition, since a nilmanifold of degree at most {d} can be filtered using the lower central series, and any linear sequence {n \mapsto g^n g_0} will be a polynomial sequence with respect to that filtration. The converse implication is a little trickier, but still not too hard to show: see Appendix C of this paper of Ben Green, Tamar Ziegler, and myself. There are two key examples of basic nilsequences to keep in mind. The first are the polynomially quasiperiodic sequences

\displaystyle  a(n) = F( P_1(n), \dots, P_k(n) ),

where {P_1,\dots,P_k: {\bf Z} \rightarrow {\bf R}} are polynomials of degree at most {d}, and {F: {\bf R}^k \rightarrow {\bf C}^m} is a {{\bf Z}^k}-automorphic (i.e., {{\bf Z}^k}-periodic) continuous function. The map {P: {\bf Z} \rightarrow {\bf R}^k} defined by {P(n) := (P_1(n),\dots,P_k(n))} is a polynomial map of degree at most {d}, if one filters {{\bf R}^k} by defining {({\bf R}^k)_i} to equal {{\bf R}^k} when {i \leq d}, and {\{0\}} for {i > d}. The torus {{\bf R}^k/{\bf Z}^k} then becomes a filtered nilmanifold of degree at most {d}, and {a(n)} is thus a basic nilsequence of degree at most {d} as per the second definition. It is also possible explicitly describe {a_n} as a basic nilsequence of degree at most {d} as per the first definition, for instance (in the {k=1} case) by taking {G} to be the space of upper triangular unipotent {d+1 \times d+1} real matrices, and {\Gamma} the subgroup with integer coefficients; we leave the details to the interested reader.

The other key example is a sequence of the form

\displaystyle  a(n) = F( \alpha n, \{ \alpha n \} \beta n )

where {\alpha,\beta} are real numbers, {\{ \alpha n \} = \alpha n - \lfloor \alpha n \rfloor} denotes the fractional part of {\alpha n}, and and {F: {\bf R}^2 \rightarrow {\bf C}^m} is a {{\bf Z}^2}-automorphic continuous function that vanishes in a neighbourhood of {{\bf Z} \times {\bf R}}. To describe this as a nilsequence, we use the nilpotent connected, simply connected degree {2}, Heisenberg group

\displaystyle  G := \begin{pmatrix} 1 & {\bf R} & {\bf R} \\ 0 & 1 & {\bf R} \\ 0 & 0 & 1 \end{pmatrix}

with the lower central series filtration {G_0=G_1=G}, {G_2= [G,G] = \begin{pmatrix} 1 &0 & {\bf R} \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{pmatrix}}, and {G_i = \{ \mathrm{id} \}} for {i > 2}, {\Gamma} to be the discrete compact subgroup

\displaystyle  \Gamma := \begin{pmatrix} 1 & {\bf Z} & {\bf Z} \\ 0 & 1 & {\bf Z} \\ 0 & 0 & 1 \end{pmatrix},

{g: {\bf Z} \rightarrow G} to be the polynomial sequence

\displaystyle  g(n) := \begin{pmatrix} 1 & \beta n & \alpha \beta n^2 \\ 0 & 1 & \alpha n \\ 0 & 0 & 1 \end{pmatrix}

and {\tilde F: G \rightarrow {\bf C}^m} to be the {\Gamma}-automorphic function

\displaystyle  \tilde F( \begin{pmatrix} 1 & x & y \\ 0 & 1 & z \\ 0 & 0 & 1 \end{pmatrix} ) = F( \{ z \}, y - \lfloor z \rfloor x );

one easily verifies that this function is indeed {\Gamma}-automorphic, and it is continuous thanks to the vanishing properties of {F}. Also we have {a(n) = \tilde F(g(n))}, so {a} is a basic nilsequence of degree at most {2}. One can concoct similar examples with {\{ \alpha n \} \beta n} replaced by other “bracket polynomials” of {n}; for instance

\displaystyle  a(n) = F( \alpha n, \{ \alpha n - \frac{1}{2} \} \beta n )

will be a basic nilsequence if {F} now vanishes in a neighbourhood of {(\frac{1}{2}+{\bf Z}) \times {\bf R}} rather than {{\bf Z} \times {\bf R}}. See this paper of Bergelson and Leibman for more discussion of bracket polynomials (also known as generalised polynomials) and their relationship to nilsequences.

A nilsequence of degree at most {d} is defined to be a sequence that is the uniform limit of basic nilsequences of degree at most {d}. Thus for instance a sequence is a nilsequence of degree at most {1} if and only if it is almost periodic, while a sequence is a nilsequence of degree at most {0} if and only if it is constant. Such objects arise in higher order recurrence: for instance, if {h_0,\dots,h_d} are integers, {(X,\mu,T)} is a measure-preserving system, and {f_0,\dots,f_d \in L^\infty(X)}, then it was shown by Leibman that the sequence

\displaystyle  n \mapsto \int_X f_0(T^{h_0 n} x) \dots f_d(T^{h_d n} x)\ d\mu(x)

is equal to a nilsequence of degree at most {d}, plus a null sequence. (The special case when the measure-preserving system was ergodic and {h_i = i} for {i=0,\dots,d} was previously established by Bergelson, Host, and Kra.) Nilsequences also arise in the inverse theory of the Gowers uniformity norms, as discussed for instance in this previous post.

It is easy to see that a sequence {a: {\bf Z} \rightarrow {\bf C}^m} is a basic nilsequence of degree at most {d} if and only if each of its {m} components are. The scalar basic nilsequences {a: {\bf Z} \rightarrow {\bf C}} of degree {d} are easily seen to form a {*}-algebra (that is to say, they are a complex vector space closed under pointwise multiplication and complex conjugation), which implies similarly that vector-valued basic nilsequences {a: {\bf Z} \rightarrow {\bf C}^m} of degree at most {d} form a complex vector space closed under complex conjugation for each {m}, and that the tensor product of any two basic nilsequences of degree at most {d} is another basic nilsequence of degree at most {d}. Similarly with “basic nilsequence” replaced by “nilsequence” throughout.

Now we turn to the notion of a nilcharacter, as defined in this paper of Ben Green, Tamar Ziegler, and myself:

Definition 3 (Nilcharacters) Let {d \geq 1}. A sub-nilcharacter of degree {d} is a basic nilsequence {\chi: n \mapsto F(g(n))} of degree at most {d}, such that {F} obeys the additional modulation property

\displaystyle  F( g_d g ) = e( \xi \cdot g_d ) F(g) \ \ \ \ \ (1)

for all {g \in G} and {g_d \in G_d}, where {\xi: G_d \rightarrow {\bf R}} is a continuous homomorphism {g_d \mapsto \xi \cdot g_d}. (Note from (1) and {\Gamma}-automorphy that unless {F} vanishes identically, {\xi} must map {\Gamma_d} to {{\bf Z}}, thus without loss of generality one can view {\xi} as an element of the Pontryagial dual of the torus {G_d / \Gamma_d}.) If in addition one has {\|F(g)\|=1} for all {g \in G}, we call {\chi} a nilcharacter of degree {d \geq 1}.

In the degree one case {d=1}, the only sub-nilcharacters are of the form {\chi(n) = e(\alpha n)} for some vector {c \in {\bf C}^m} and {\alpha \in {\bf R}}, and this is a nilcharacter if {c} is a unit vector. Similarly, in higher degree, any sequence of the form {\chi(n) = c e(P(n))}, where {c \in {\bf C}^m} is a vector and {P: {\bf Z} \rightarrow {\bf R}} is a polynomial of degree at most {d}, is a sub-nilcharacter of degree {d}, and a character if {c} is a unit vector. A nilsequence of degree at most {d-1} is automatically a sub-nilcharacter of degree {d}, and a nilcharacter if it is of magnitude {1}. A further example of a nilcharacter is provided by the two-dimensional sequence {\chi: {\bf Z} \rightarrow {\bf C}^2} defined by

\displaystyle  \chi(n) := ( F_0( \alpha n ) e( \{ \alpha n \} \beta n ), F_{1/2}( \alpha n ) e( \{ \alpha n - \frac{1}{2} \} \beta n ) ) \ \ \ \ \ (2)

where {F_0, F_{1/2}: {\bf R} \rightarrow {\bf C}} are continuous, {{\bf Z}}-automorphic functions that vanish on a neighbourhood of {{\bf Z}} and {\frac{1}{2}+{\bf Z}} respectively, and which form a partition of unity in the sense that

\displaystyle  |F_0(x)|^2 + |F_{1/2}(x)|^2 = 1

for all {x \in {\bf R}}. Note that one needs both {F_0} and {F_{1/2}} to be not identically zero in order for all these conditions to be satisfied; it turns out (for topological reasons) that there is no scalar nilcharacter that is “equivalent” to this nilcharacter in a sense to be defined shortly. In some literature, one works exclusively with sub-nilcharacters rather than nilcharacters, however the former space contains zero-divisors, which is a little annoying technically. Nevertheless, both nilcharacters and sub-nilcharacters generate the same set of “symbols” as we shall see later.

We claim that every degree {d} sub-nilcharacter {f: {\bf Z} \rightarrow {\bf C}^m} can be expressed in the form {f = c \chi}, where {\chi: {\bf Z} \rightarrow {\bf C}^{m'}} is a degree {d} nilcharacter, and {c: {\bf C}^{m'} \rightarrow {\bf C}^m} is a linear transformation. Indeed, by scaling we may assume {f(n) = F(g(n))} where {|F| < 1} uniformly. Using partitions of unity, one can find further functions {F_1,\dots,F_m} also obeying (1) for the same character {\xi} such that {|F_1|^2 + \dots + |F_m|^2} is non-zero; by dividing out the {F_1,\dots,F_m} by the square root of this quantity, and then multiplying by {\sqrt{1-|F|^2}}, we may assume that

\displaystyle  |F|^2 + |F_1|^2 + \dots + |F_m|^2 = 1,

and then

\displaystyle \chi(n) := (F(g(n)), F_1(g(n)), \dots, F_m(g(n)))

becomes a degree {d} nilcharacter that contains {f(n)} amongst its components, giving the claim.

As we shall show below, nilsequences can be approximated uniformly by linear combinations of nilcharacters, in much the same way that quasiperiodic or almost periodic sequences can be approximated uniformly by linear combinations of linear phases. In particular, nilcharacters can be used as “obstructions to uniformity” in the sense of the inverse theory of the Gowers uniformity norms.

The space of degree {d} nilcharacters forms a semigroup under tensor product, with the constant sequence {1} as the identity. One can upgrade this semigroup to an abelian group by quotienting nilcharacters out by equivalence:

Definition 4 Let {d \geq 1}. We say that two degree {d} nilcharacters {\chi: {\bf Z} \rightarrow {\bf C}^m}, {\chi': {\bf Z} \rightarrow {\bf C}^{m'}} are equivalent if {\chi \otimes \overline{\chi'}: {\bf Z} \rightarrow {\bf C}^{mm'}} is equal (as a sequence) to a basic nilsequence of degree at most {d-1}. (We will later show that this is indeed an equivalence relation.) The equivalence class {[\chi]_{\mathrm{Symb}^d({\bf Z})}} of such a nilcharacter will be called the symbol of that nilcharacter (in analogy to the symbol of a differential or pseudodifferential operator), and the collection of such symbols will be denoted {\mathrm{Symb}^d({\bf Z})}.

As we shall see below the fold, {\mathrm{Symb}^d({\bf Z})} has the structure of an abelian group, and enjoys some nice “symbol calculus” properties; also, one can view symbols as precisely describing the obstruction to equidistribution for nilsequences. For {d=1}, the group is isomorphic to the Ponytragin dual {\hat {\bf Z} = {\bf R}/{\bf Z}} of the integers, and {\mathrm{Symb}^d({\bf Z})} for {d > 1} should be viewed as higher order generalisations of this Pontryagin dual. In principle, this group can be explicitly described for all {d}, but the theory rapidly gets complicated as {d} increases (much as the classification of nilpotent Lie groups or Lie algebras of step {d} rapidly gets complicated even for medium-sized {d} such as {d=3} or {d=4}). We will give an explicit description of the {d=2} case here. There is however one nice (and non-trivial) feature of {\mathrm{Symb}^d({\bf Z})} for {d \geq 2} – it is not just an abelian group, but is in fact a vector space over the rationals {{\bf Q}}!

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Van Vu and I just posted to the arXiv our paper “sum-free sets in groups” (submitted to Discrete Analysis), as well as a companion survey article (submitted to J. Comb.). Given a subset {A} of an additive group {G = (G,+)}, define the quantity {\phi(A)} to be the cardinality of the largest subset {B} of {A} which is sum-free in {A} in the sense that all the sums {b_1+b_2} with {b_1,b_2} distinct elements of {B} lie outside of {A}. For instance, if {A} is itself a group, then {\phi(A)=1}, since no two elements of {A} can sum to something outside of {A}. More generally, if {A} is the union of {k} groups, then {\phi(A)} is at most {k}, thanks to the pigeonhole principle.

If {G} is the integers, then there are no non-trivial subgroups, and one can thus expect {\phi(A)} to start growing with {A}. For instance, one has the following easy result:

Proposition 1 Let {A} be a set of {2^k} natural numbers. Then {\phi(A) > k}.

Proof: We use an argument of Ruzsa, which is based in turn on an older argument of Choi. Let {x_1} be the largest element of {A}, and then recursively, once {x_1,\dots,x_i} has been selected, let {x_{i+1}} be the largest element of {A} not equal to any of the {x_1,\dots,x_i}, such that {x_{i+1}+x_j \not \in A} for all {j=1,\dots,i}, terminating this construction when no such {x_{i+1}} can be located. This gives a sequence {x_1 > x_2 > \dots > x_m} of elements in {A} which are sum-free in {A}, and with the property that for any {y \in A}, either {y} is equal to one of the {x_i}, or else {y + x_i \in A} for some {i} with {x_i > y}. Iterating this, we see that any {y \in A} is of the form {x_{i_1} - x_{i_2} - \dots - x_{i_j}} for some {j \geq 1} and {1 \leq i_1 < i_2 < \dots \leq i_j \leq m}. The number of such expressions {x_{i_1} - x_{i_2} - \dots - x_{i_j}} is at most {2^{m}-1}, thus {2^k \leq 2^m-1} which implies {m \geq k+1}. Since {\phi(A) \geq m}, the claim follows. \Box

In particular, we have {\phi(A) \gg \log |A|} for subsets {A} of the integers. It has been possible to improve upon this easy bound, but only with remarkable effort. The best lower bound currently is

\displaystyle \phi(A) \geq \log |A| (\log\log|A|)^{1/2 - o(1)},

a result of Shao (building upon earlier work of Sudakov, Szemeredi, and Vu and of Dousse). In the opposite direction, a construction of Ruzsa gives examples of large sets {A} with {\phi(A) \leq \exp( O( \sqrt{\log |A|} ) )}.

Using the standard tool of Freiman homomorphisms, the above results for the integers extend to other torsion-free abelian groups {G}. In our paper we study the opposite case where {G} is finite (but still abelian). In this paper of Erdös (in which the quantity {\phi(A)} was first introduced), the following question was posed: if {A} is sufficiently large depending on {\phi(A)}, does this imply the existence of two elements {x,y \in A} with {x+y=0}? As it turns out, we were able to find some simple counterexamples to this statement. For instance, if {H} is any finite additive group, then the set {A := \{ 1 \hbox{ mod } 7, 2 \hbox{ mod } 7, 4 \hbox{ mod } 7\} \times H \subset {\bf Z}/7{\bf Z} \times H} has {\phi(A)=3} but with no {x,y \in A} summing to zero; this type of example in fact works with {7} replaced by any larger Mersenne prime, and we also have a counterexample in {{\bf Z}/2^n{\bf Z}} for {n} arbitrarily large. However, in the positive direction, we can show that the answer to Erdös’s question is positive if {|G|} is assumed to have no small prime factors. That is to say,

Theorem 2 For every {k \geq 1} there exists {C \geq 1} such that if {G} is a finite abelian group whose order is not divisible by any prime less than or equal to {C}, and {A} is a subset of {G} with order at least {C} and {\phi(A) \leq k}, then there exist {x,y \in A} with {x+y=0}.

There are two main tools used to prove this result. One is an “arithmetic removal lemma” proven by Král, Serra, and Vena. Note that the condition {\phi(A) \leq k} means that for any distinct {x_1,\dots,x_{k+1} \in A}, at least one of the {x_i+x_j}, {1 \leq i < j \leq k+1}, must also lie in {A}. Roughly speaking, the arithmetic removal lemma allows one to “almost” remove the requirement that {x_1,\dots,x_{k+1}} be distinct, which basically now means that {x \in A \implies 2x \in A} for almost all {x \in A}. This near-dilation symmetry, when combined with the hypothesis that {|G|} has no small prime factors, gives a lot of “dispersion” in the Fourier coefficients of {1_A} which can now be exploited to prove the theorem.

The second tool is the following structure theorem, which is the main result of our paper, and goes a fair ways towards classifying sets {A} for which {\phi(A)} is small:

Theorem 3 Let {A} be a finite subset of an arbitrary additive group {G}, with {\phi(A) \leq k}. Then one can find finite subgroups {H_1,\dots,H_m} with {m \leq k} such that {|A \cap H_i| \gg_k |H_i|} and {|A \backslash (H_1 \cup \dots \cup H_m)| \ll_k 1}. Furthermore, if {m=k}, then the exceptional set {A \backslash (H_1 \cup \dots \cup H_m)} is empty.

Roughly speaking, this theorem shows that the example of the union of {k} subgroups mentioned earlier is more or less the “only” example of sets {A} with {\phi(A) \leq k}, modulo the addition of some small exceptional sets and some refinement of the subgroups to dense subsets.

This theorem has the flavour of other inverse theorems in additive combinatorics, such as Freiman’s theorem, and indeed one can use Freiman’s theorem (and related tools, such as the Balog-Szemeredi theorem) to easily get a weaker version of this theorem. Indeed, if there are no sum-free subsets of {A} of order {k+1}, then a fraction {\gg_k 1} of all pairs {a,b} in {A} must have their sum also in {A} (otherwise one could take {k+1} random elements of {A} and they would be sum-free in {A} with positive probability). From this and the Balog-Szemeredi theorem and Freiman’s theorem (in arbitrary abelian groups, as established by Green and Ruzsa), we see that {A} must be “commensurate” with a “coset progression” {H+P} of bounded rank. One can then eliminate the torsion-free component {P} of this coset progression by a number of methods (e.g. by using variants of the argument in Proposition 1), with the upshot being that one can locate a finite group {H_1} that has large intersection with {A}.

At this point it is tempting to simply remove {H_1} from {A} and iterate. But one runs into a technical difficulty that removing a set such as {H_1} from {A} can alter the quantity {\phi(A)} in unpredictable ways, so one has to still keep {H_1} around when analysing the residual set {A \backslash H_1}. A second difficulty is that the latter set {A \backslash H_1} could be considerably smaller than {A} or {H_1}, but still large in absolute terms, so in particular any error term whose size is only bounded by {\varepsilon |A|} for a small {\varepsilon} could be massive compared with the residual set {A\backslash H_1}, and so such error terms would be unacceptable. One can get around these difficulties if one first performs some preliminary “normalisation” of the group {H_1}, so that the residual set {A \backslash H_1} does not intersect any coset of {H_1} too strongly. The arguments become even more complicated when one starts removing more than one group {H_1,\dots,H_i} from {A} and analyses the residual set {A \backslash (H_1 \cup \dots \cup H_i)}; indeed the “epsilon management” involved became so fearsomely intricate that we were forced to use a nonstandard analysis formulation of the problem in order to keep the complexity of the argument at a reasonable level (cf. my previous blog post on this topic). One drawback of doing so is that we have no effective bounds for the implied constants in our main theorem; it would be of interest to obtain a more direct proof of our main theorem that would lead to effective bounds.

I’ve just uploaded to the arXiv my paper “Inverse theorems for sets and measures of polynomial growth“. This paper was motivated by two related questions. The first question was to obtain a qualitatively precise description of the sets of polynomial growth that arise in Gromov’s theorem, in much the same way that Freiman’s theorem (and its generalisations) provide a qualitatively precise description of sets of small doubling. The other question was to obtain a non-abelian analogue of inverse Littlewood-Offord theory.

Let me discuss the former question first. Gromov’s theorem tells us that if a finite subset {A} of a group {G} exhibits polynomial growth in the sense that {|A^n|} grows polynomially in {n}, then the group generated by {A} is virtually nilpotent (the converse direction also true, and is relatively easy to establish). This theorem has been strengthened a number of times over the years. For instance, a few years ago, I proved with Shalom that the condition that {|A^n|} grew polynomially in {n} could be replaced by {|A^n| \leq C n^d} for a single {n}, as long as {n} was sufficiently large depending on {C,d} (in fact we gave a fairly explicit quantitative bound on how large {n} needed to be). A little more recently, with Breuillard and Green, the condition {|A^n| \leq C n^d} was weakened to {|A^n| \leq n^d |A|}, that is to say it sufficed to have polynomial relative growth at a finite scale. In fact, the latter paper gave more information on {A} in this case, roughly speaking it showed (at least in the case when {A} was a symmetric neighbourhood of the identity) that {A^n} was “commensurate” with a very structured object known as a coset nilprogression. This can then be used to establish further control on {A}. For instance, it was recently shown by Breuillard and Tointon (again in the symmetric case) that if {|A^n| \leq n^d |A|} for a single {n} that was sufficiently large depending on {d}, then all the {A^{n'}} for {n' \geq n} have a doubling constant bounded by a bound {C_d} depending only on {d}, thus {|A^{2n'}| \leq C_d |A^{n'}|} for all {n' \geq n}.

In this paper we are able to refine this analysis a bit further; under the same hypotheses, we can show an estimate of the form

\displaystyle  \log |A^{n'}| = \log |A^n| + f( \log n' - \log n ) + O_d(1)

for all {n' \geq n} and some piecewise linear, continuous, non-decreasing function {f: [0,+\infty) \rightarrow [0,+\infty)} with {f(0)=0}, where the error {O_d(1)} is bounded by a constant depending only on {d}, and where {f} has at most {O_d(1)} pieces, each of which has a slope that is a natural number of size {O_d(1)}. To put it another way, the function {n' \mapsto |A^{n'}|} for {n' \geq n} behaves (up to multiplicative constants) like a piecewise polynomial function, where the degree of the function and number of pieces is bounded by a constant depending on {d}.

One could ask whether the function {f} has any convexity or concavity properties. It turns out that it can exhibit either convex or concave behaviour (or a combination of both). For instance, if {A} is contained in a large finite group, then {n \mapsto |A^n|} will eventually plateau to a constant, exhibiting concave behaviour. On the other hand, in nilpotent groups one can see convex behaviour; for instance, in the Heisenberg group {\begin{pmatrix}{} {1} {\mathbf Z} {\mathbf Z} \\ {0} {1} {\mathbf Z} \\ {0} {1} \end{pmatrix}}, if one sets {A} to be a set of matrices of the form {\begin{pmatrix} 1 & O(N) & O(N^3) \\ 0 & 1 & O(N) \\ 0 & 0 & 1 \end{pmatrix}} for some large {N} (abusing the {O()} notation somewhat), then {n \mapsto A^n} grows cubically for {n \leq N} but then grows quartically for {n > N}.

To prove this proposition, it turns out (after using a somewhat difficult inverse theorem proven previously by Breuillard, Green, and myself) that one has to analyse the volume growth {n \mapsto |P^n|} of nilprogressions {P}. In the “infinitely proper” case where there are no unexpected relations between the generators of the nilprogression, one can lift everything to a simply connected Lie group (where one can take logarithms and exploit the Baker-Campbell-Hausdorff formula heavily), eventually describing {P^n} with fair accuracy by a certain convex polytope with vertices depending polynomially on {n}, which implies that {|P^n|} depends polynomially on {n} up to constants. If one is not in the “infinitely proper” case, then at some point {n_0} the nilprogression {P^{n_0}} develops a “collision”, but then one can use this collision to show (after some work) that the dimension of the “Lie model” of {P^{n_0}} has dropped by at least one from the dimension of {P} (the notion of a Lie model being developed in the previously mentioned paper of Breuillard, Greenm, and myself), so that this sort of collision can only occur a bounded number of times, with essentially polynomial volume growth behaviour between these collisions.

The arguments also give a precise description of the location of a set {A} for which {A^n} grows polynomially in {n}. In the symmetric case, what ends up happening is that {A^n} becomes commensurate to a “coset nilprogression” {HP} of bounded rank and nilpotency class, whilst {A} is “virtually” contained in a scaled down version {HP^{1/n}} of that nilprogression. What “virtually” means is a little complicated; roughly speaking, it means that there is a set {X} of bounded cardinality such that {aXHP^{1/n} \approx XHP^{1/n}} for all {a \in A}. Conversely, if {A} is virtually contained in {HP^{1/n}}, then {A^n} is commensurate to {HP} (and more generally, {A^{mn}} is commensurate to {HP^m} for any natural number {m}), giving quite a (qualitatively) precise description of {A} in terms of coset nilprogressions.

The main tool used to prove these results is the structure theorem for approximate groups established by Breuillard, Green, and myself, which roughly speaking asserts that approximate groups are always commensurate with coset nilprogressions. A key additional trick is a pigeonholing argument of Sanders, which in this context is the assertion that if {A^n} is comparable to {A^{2n}}, then there is an {n'} between {n} and {2n} such that {A \cdot A^{n'}} is very close in size to {A^{n'}} (up to a relative error of {1/n}). It is this fact, together with the comparability of {A^{n'}} to a coset nilprogression {HP}, that allows us (after some combinatorial argument) to virtually place {A} inside {HP^{1/n}}.

Similar arguments apply when discussing iterated convolutions {\mu^{*n}} of (symmetric) probability measures on a (discrete) group {G}, rather than combinatorial powers {A^n} of a finite set. Here, the analogue of volume {A^n} is given by the negative power {\| \mu^{*n} \|_{\ell^2}^{-2}} of the {\ell^2} norm of {\mu^{*n}} (thought of as a non-negative function on {G} of total mass 1). One can also work with other norms here than {\ell^2}, but this norm has some minor technical conveniences (and other measures of the “spread” of {\mu^{*n}} end up being more or less equivalent for our purposes). There is an analogous structure theorem that asserts that if {\mu^{*n}} spreads at most polynomially in {n}, then {\mu^{*n}} is “commensurate” with the uniform probability distribution on a coset progression {HP}, and {\mu} itself is largely concentrated near {HP^{1/\sqrt{n}}}. The factor of {\sqrt{n}} here is the familiar scaling factor in random walks that arises for instance in the central limit theorem. The proof of (the precise version of) this statement proceeds similarly to the combinatorial case, using pigeonholing to locate a scale {n'} where {\mu *\mu^{n'}} has almost the same {\ell^2} norm as {\mu^{n'}}.

A special case of this theory occurs when {\mu} is the uniform probability measure on {n} elements {v_1,\dots,v_n} of {G} and their inverses. The probability measure {\mu^{*n}} is then the distribution of a random product {w_1 \dots w_n}, where each {w_i} is equal to one of {v_{j_i}} or its inverse {v_{j_i}^{-1}}, selected at random with {j_i} drawn uniformly from {\{1,\dots,n\}} with replacement. This is very close to the Littlewood-Offord situation of random products {u_1 \dots u_n} where each {u_i} is equal to {v_i} or {v_i^{-1}} selected independently at random (thus {j_i} is now fixed to equal {i} rather than being randomly drawn from {\{1,\dots,n\}}. In the case when {G} is abelian, it turns out that a little bit of Fourier analysis shows that these two random walks have “comparable” distributions in a certain {\ell^2} sense. As a consequence, the results in this paper can be used to recover an essentially optimal abelian inverse Littlewood-Offord theorem of Nguyen and Vu. In the nonabelian case, the only Littlewood-Offord theorem I am aware of is a recent result of Tiep and Vu for matrix groups, but in this case I do not know how to relate the above two random walks to each other, and so we can only obtain an analogue of the Tiep-Vu results for the symmetrised random walk {w_1 \dots w_n} instead of the ordered random walk {u_1 \dots u_n}.

Suppose that {A \subset B} are two subgroups of some ambient group {G}, with the index {K := [B:A]} of {A} in {B} being finite. Then {B} is the union of {K} left cosets of {A}, thus {B = SA} for some set {S \subset B} of cardinality {K}. The elements {s} of {S} are not entirely arbitrary with regards to {A}. For instance, if {A} is a normal subgroup of {B}, then for each {s \in S}, the conjugation map {g \mapsto s^{-1} g s} preserves {A}. In particular, if we write {A^s := s^{-1} A s} for the conjugate of {A} by {s}, then

\displaystyle  A = A^s.

Even if {A} is not normal in {B}, it turns out that the conjugation map {g \mapsto s^{-1} g s} approximately preserves {A}, if {K} is bounded. To quantify this, let us call two subgroups {A,B} {K}-commensurate for some {K \geq 1} if one has

\displaystyle  [A : A \cap B], [B : A \cap B] \leq K.

Proposition 1 Let {A \subset B} be groups, with finite index {K = [B:A]}. Then for every {s \in B}, the groups {A} and {A^s} are {K}-commensurate, in fact

\displaystyle  [A : A \cap A^s ] = [A^s : A \cap A^s ] \leq K.

Proof: One can partition {B} into {K} left translates {xA} of {A}, as well as {K} left translates {yA^s} of {A^s}. Combining the partitions, we see that {B} can be partitioned into at most {K^2} non-empty sets of the form {xA \cap yA^s}. Each of these sets is easily seen to be a left translate of the subgroup {A \cap A^s}, thus {[B: A \cap A^s] \leq K^2}. Since

\displaystyle [B: A \cap A^s] = [B:A] [A: A \cap A^s] = [B:A^s] [A^s: A \cap A^s]

and {[B:A] = [B:A^s]=K}, we obtain the claim. \Box

One can replace the inclusion {A \subset B} by commensurability, at the cost of some worsening of the constants:

Corollary 2 Let {A, B} be {K}-commensurate subgroups of {G}. Then for every {s \in B}, the groups {A} and {A^s} are {K^2}-commensurate.

Proof: Applying the previous proposition with {A} replaced by {A \cap B}, we see that for every {s \in B}, {A \cap B} and {(A \cap B)^s} are {K}-commensurate. Since {A \cap B} and {(A \cap B)^s} have index at most {K} in {A} and {A^s} respectively, the claim follows. \Box

It turns out that a similar phenomenon holds for the more general concept of an approximate group, and gives a “classification” of all the approximate groups {B} containing a given approximate group {A} as a “bounded index approximate subgroup”. Recall that a {K}-approximate group {A} in a group {G} for some {K \geq 1} is a symmetric subset of {G} containing the identity, such that the product set {A^2 := \{ a_1 a_2: a_1,a_2 \in A\}} can be covered by at most {K} left translates of {A} (and thus also {K} right translates, by the symmetry of {A}). For simplicity we will restrict attention to finite approximate groups {A} so that we can use their cardinality {A} as a measure of size. We call finite two approximate groups {A,B} {K}-commensurate if one has

\displaystyle  |A^2 \cap B^2| \geq \frac{1}{K} |A|, \frac{1}{K} |B|;

note that this is consistent with the previous notion of commensurability for genuine groups.

Theorem 3 Let {G} be a group, and let {K_1,K_2,K_3 \geq 1} be real numbers. Let {A} be a finite {K_1}-approximate group, and let {B} be a symmetric subset of {G} that contains {A}.

  • (i) If {B} is a {K_2}-approximate group with {|B| \leq K_3 |A|}, then one has {B \subset SA} for some set {S} of cardinality at most {K_1 K_2 K_3}. Furthermore, for each {s \in S}, the approximate groups {A} and {A^s} are {K_1 K_2^5 K_3}-commensurate.
  • (ii) Conversely, if {B \subset SA} for some set {S} of cardinality at most {K_3}, and {A} and {A^s} are {K_2}-commensurate for all {s \in S}, then {|B| \leq K_3 |A|}, and {B} is a {K_1^6 K_2 K_3^2}-approximate group.

Informally, the assertion that {B} is an approximate group containing {A} as a “bounded index approximate subgroup” is equivalent to {B} being covered by a bounded number of shifts {sA} of {A}, where {s} approximately normalises {A^2} in the sense that {A^2} and {(A^2)^s} have large intersection. Thus, to classify all such {B}, the problem essentially reduces to that of classifying those {s} that approximately normalise {A^2}.

To prove the theorem, we recall some standard lemmas from arithmetic combinatorics, which are the foundation stones of the “Ruzsa calculus” that we will use to establish our results:

Lemma 4 (Ruzsa covering lemma) If {A} and {B} are finite non-empty subsets of {G}, then one has {B \subset SAA^{-1}} for some set {S \subset B} with cardinality {|S| \leq \frac{|BA|}{|A|}}.

Proof: We take {S} to be a subset of {B} with the property that the translates {sA, s \in S} are disjoint in {BA}, and such that {S} is maximal with respect to set inclusion. The required properties of {S} are then easily verified. \Box

Lemma 5 (Ruzsa triangle inequality) If {A,B,C} are finite non-empty subsets of {G}, then

\displaystyle  |A C^{-1}| \leq |A B^{-1}| |B C^{-1}| / |B|.

Proof: If {ac^{-1}} is an element of {AC^{-1}} with {a \in A} and {c \in C}, then from the identity {ac^{-1} = (ab^{-1}) (bc^{-1})} we see that {ac^{-1}} can be written as the product of an element of {AB^{-1}} and an element of {BC^{-1}} in at least {|B|} distinct ways. The claim follows. \Box

Now we can prove (i). By the Ruzsa covering lemma, {B} can be covered by at most

\displaystyle \frac{|BA|}{|A|} \leq \frac{|B^2|}{|A|} \leq \frac{K_2 |B|}{|A|} \leq K_2 K_3

left-translates of {A^2}, and hence by at most {K_1 K_2 K_3} left-translates of {A}, thus {B \subset SA} for some {|S| \leq K_1 K_2 K_3}. Since {sA} only intersects {B} if {s \in BA}, we may assume that

\displaystyle  S \subset BA \subset B^2

and hence for any {s \in S}

\displaystyle  |A^s A| \leq |B^2 A B^2 A| \leq |B^6|

\displaystyle  \leq K_2^5 |B| \leq K_2^5 K_3 |A|.

By the Ruzsa covering lemma again, this implies that {A^s} can be covered by at most {K_2^5 K_3} left-translates of {A^2}, and hence by at most {K_1 K_2^5 K_3} left-translates of {A}. By the pigeonhole principle, there thus exists a group element {g} with

\displaystyle  |A^s \cap gA| \geq \frac{1}{K_1 K_2^5 K_3} |A|.


\displaystyle  |A^s \cap gA| \leq | (A^s \cap gA)^{-1} (A^s \cap gA)|


\displaystyle  (A^s \cap gA)^{-1} (A^s \cap gA) \subset A^2 \cap (A^s)^2

the claim follows.

Now we prove (ii). Clearly

\displaystyle  |B| \leq |S| |A| \leq K_3 |A|.

Now we control the size of {B^2 A}. We have

\displaystyle  |B^2 A| \leq |SA SA^2| \leq K_3^2 \sup_{s \in S} |A s A^2| = K_3^2 \sup_{s \in S} |A^s A^2|.

From the Ruzsa triangle inequality and symmetry we have

\displaystyle  |A^s A^2| \leq \frac{ |A^s (A^2 \cap (A^2)^s)| |(A^2 \cap (A^2)^s) A^2|}{|A^2 \cap (A^2)^s|}

\displaystyle  \leq \frac{ |(A^3)^s| |A^4| }{|A|/K_2}

\displaystyle  \leq K_2 \frac{ |A^3| |A^4| }{|A|}

\displaystyle  \leq K_1^5 K_2 |A|

and thus

\displaystyle  |B^2 A| \leq K_1^5 K_2 K_3^2 |A|.

By the Ruzsa covering lemma, this implies that {B^2} is covered by at most {K_1^5 K_2 K_3^2} left-translates of {A^2}, hence by at most {K_1^6 K_2 K_3^2} left-translates of {A}. Since {A \subset B}, the claim follows.

We now establish some auxiliary propositions about commensurability of approximate groups. The first claim is that commensurability is approximately transitive:

Proposition 6 Let {A} be a {K_1}-approximate group, {B} be a {K_2}-approximate group, and {C} be a {K_3}-approximate group. If {A} and {B} are {K_4}-commensurate, and {B} and {C} are {K_5}-commensurate, then {A} and {C} are {K_1^2 K_2^3 K_2^3 K_4 K_5 \max(K_1,K_3)}-commensurate.

Proof: From two applications of the Ruzsa triangle inequality we have

\displaystyle  |AC| \leq \frac{|A (A^2 \cap B^2)| |(A^2 \cap B^2) (B^2 \cap C^2)| |(B^2 \cap C^2) C|}{|A^2 \cap B^2| |B^2 \cap C^2|}

\displaystyle  \leq \frac{|A^3| |B^4| |C^3|}{ (|A|/K_4) (|B|/K_5)}

\displaystyle  \leq K_4 K_5 \frac{K_1^2 |A| K_2^3 |B| K_3^2 |C|}{ |A| |B| }

\displaystyle  = K_1^2 K_2^3 K_3^2 K_4 K_5 |C|.

By the Ruzsa covering lemma, we may thus cover {A} by at most {K_1^2 K_2^3 K_3^2 K_4 K_5} left-translates of {C^2}, and hence by {K_1^2 K_2^3 K_3^3 K_4 K_5} left-translates of {C}. By the pigeonhole principle, there thus exists a group element {g} such that

\displaystyle  |A \cap gC| \geq \frac{1}{K_1^2 K_2^3 K_3^3 K_4 K_5} |A|,

and so by arguing as in the proof of part (i) of the theorem we have

\displaystyle  |A^2 \cap C^2| \geq \frac{1}{K_1^2 K_2^3 K_3^3 K_4 K_5} |A|

and similarly

\displaystyle  |A^2 \cap C^2| \geq \frac{1}{K_1^3 K_2^3 K_3^2 K_4 K_5} |C|

and the claim follows. \Box

The next proposition asserts that the union and (modified) intersection of two commensurate approximate groups is again an approximate group:

Proposition 7 Let {A} be a {K_1}-approximate group, {B} be a {K_2}-approximate group, and suppose that {A} and {B} are {K_3}-commensurate. Then {A \cup B} is a {K_1 + K_2 + K_1^2 K_2^4 K_3 + K_1^4 K_2^2 K_3}-approximate subgroup, and {A^2 \cap B^2} is a {K_1^6 K_2^3 K_3}-approximate subgroup.

Using this proposition, one may obtain a variant of the previous theorem where the containment {A \subset B} is replaced by commensurability; we leave the details to the interested reader.

Proof: We begin with {A \cup B}. Clearly {A \cup B} is symmetric and contains the identity. We have {(A \cup B)^2 = A^2 \cup AB \cup BA \cup B^2}. The set {A^2} is already covered by {K_1} left translates of {A}, and hence of {A \cup B}; similarly {B^2} is covered by {K_2} left translates of {A \cup B}. As for {AB}, we observe from the Ruzsa triangle inequality that

\displaystyle  |AB^2| \leq \frac{|A (A^2 \cap B^2)| |(A^2 \cap B^2) B^2|}{|A^2 \cap B^2|}

\displaystyle  \leq \frac{|A^3| |B^4|}{|A|/K_3}

\displaystyle  \leq K_1^2 K_2^3 K_3 |B|

and hence by the Ruzsa covering lemma, {AB} is covered by at most {K_1^2 K_2^3 K_3} left translates of {B^2}, and hence by {K_1^2 K_2^4 K_3} left translates of {B}, and hence of {A \cup B}. Similarly {BA} is covered by at most {K_1^4 K_2^2 K_3} left translates of {B}. The claim follows.

Now we consider {A^2 \cap B^2}. Again, this is clearly symmetric and contains the identity. Repeating the previous arguments, we see that {A} is covered by at most {K_1^2 K_2^3 K_3} left-translates of {B}, and hence there exists a group element {g} with

\displaystyle  |A \cap gB| \geq \frac{1}{K_1^2 K_2^3 K_3} |A|.

Now observe that

\displaystyle  |(A^2 \cap B^2)^2 (A \cap gB)| \leq |A^5| \leq K_1^4 |A|

and so by the Ruzsa covering lemma, {(A^2 \cap B^2)^2} can be covered by at most {K_1^6 K_2^3 K_3} left-translates of {(A \cap gB) (A \cap gB)^{-1}}. But this latter set is (as observed previously) contained in {A^2 \cap B^2}, and the claim follows. \Box

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 Baire {\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}. Once one is in the separable case, the Baire sigma algebra is identical with the more familiar Borel sigma algebra.

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 2 (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 3 (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 4 (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 5 (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 6 (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.

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Roth’s theorem on arithmetic progressions asserts that every subset of the integers {{\bf Z}} of positive upper density contains infinitely many arithmetic progressions of length three. There are many versions and variants of this theorem. Here is one of them:

Theorem 1 (Roth’s theorem) Let {G = (G,+)} be a compact abelian group, with Haar probability measure {\mu}, which is {2}-divisible (i.e. the map {x \mapsto 2x} is surjective) and let {A} be a measurable subset of {G} with {\mu(A) \geq \alpha} for some {0 < \alpha < 1}. Then we have

\displaystyle  \int_G \int_G 1_A(x) 1_A(x+r) 1_A(x+2r)\ d\mu(x) d\mu(r) \gg_\alpha 1,

where {X \gg_\alpha Y} denotes the bound {X \geq c_\alpha Y} for some {c_\alpha > 0} depending only on {\alpha}.

This theorem is usually formulated in the case that {G} is a finite abelian group of odd order (in which case the result is essentially due to Meshulam) or more specifically a cyclic group {G = {\bf Z}/N{\bf Z}} of odd order (in which case it is essentially due to Varnavides), but is also valid for the more general setting of {2}-divisible compact abelian groups, as we shall shortly see. One can be more precise about the dependence of the implied constant {c_\alpha} on {\alpha}, but to keep the exposition simple we will work at the qualitative level here, without trying at all to get good quantitative bounds. The theorem is also true without the {2}-divisibility hypothesis, but the proof we will discuss runs into some technical issues due to the degeneracy of the {2r} shift in that case.

We can deduce Theorem 1 from the following more general Khintchine-type statement. Let {\hat G} denote the Pontryagin dual of a compact abelian group {G}, that is to say the set of all continuous homomorphisms {\xi: x \mapsto \xi \cdot x} from {G} to the (additive) unit circle {{\bf R}/{\bf Z}}. Thus {\hat G} is a discrete abelian group, and functions {f \in L^2(G)} have a Fourier transform {\hat f \in \ell^2(\hat G)} defined by

\displaystyle  \hat f(\xi) := \int_G f(x) e^{-2\pi i \xi \cdot x}\ d\mu(x).

If {G} is {2}-divisible, then {\hat G} is {2}-torsion-free in the sense that the map {\xi \mapsto 2 \xi} is injective. For any finite set {S \subset \hat G} and any radius {\rho>0}, define the Bohr set

\displaystyle  B(S,\rho) := \{ x \in G: \sup_{\xi \in S} \| \xi \cdot x \|_{{\bf R}/{\bf Z}} < \rho \}

where {\|\theta\|_{{\bf R}/{\bf Z}}} denotes the distance of {\theta} to the nearest integer. We refer to the cardinality {|S|} of {S} as the rank of the Bohr set. We record a simple volume bound on Bohr sets:

Lemma 2 (Volume packing bound) Let {G} be a compact abelian group with Haar probability measure {\mu}. For any Bohr set {B(S,\rho)}, we have

\displaystyle  \mu( B( S, \rho ) ) \gg_{|S|, \rho} 1.

Proof: We can cover the torus {({\bf R}/{\bf Z})^S} by {O_{|S|,\rho}(1)} translates {\theta+Q} of the cube {Q := \{ (\theta_\xi)_{\xi \in S} \in ({\bf R}/{\bf Z})^S: \sup_{\xi \in S} \|\theta_\xi\|_{{\bf R}/{\bf Z}} < \rho/2 \}}. Then the sets {\{ x \in G: (\xi \cdot x)_{\xi \in S} \in \theta + Q \}} form an cover of {G}. But all of these sets lie in a translate of {B(S,\rho)}, and the claim then follows from the translation invariance of {\mu}. \Box

Given any Bohr set {B(S,\rho)}, we define a normalised “Lipschitz” cutoff function {\nu_{B(S,\rho)}: G \rightarrow {\bf R}} by the formula

\displaystyle  \nu_{B(S,\rho)}(x) = c_{B(S,\rho)} (1 - \frac{1}{\rho} \sup_{\xi \in S} \|\xi \cdot x\|_{{\bf R}/{\bf Z}})_+ \ \ \ \ \ (1)

where {c_{B(S,\rho)}} is the constant such that

\displaystyle  \int_G \nu_{B(S,\rho)}\ d\mu = 1,


\displaystyle c_{B(S,\rho)} = \left( \int_{B(S,\rho)} (1 - \frac{1}{\rho} \sup_{\xi \in S} \|\xi \cdot x\|_{{\bf R}/{\bf Z}})\ d\mu(x) \right)^{-1}.

The function {\nu_{B(S,\rho)}} should be viewed as an {L^1}-normalised “tent function” cutoff to {B(S,\rho)}. Note from Lemma 2 that

\displaystyle  1 \ll_{|S|,\rho} c_{B(S,\rho)} \ll_{|S|,\rho} 1. \ \ \ \ \ (2)

We then have the following sharper version of Theorem 1:

Theorem 3 (Roth-Khintchine theorem) Let {G = (G,+)} be a {2}-divisible compact abelian group, with Haar probability measure {\mu}, and let {\epsilon>0}. Then for any measurable function {f: G \rightarrow [0,1]}, there exists a Bohr set {B(S,\rho)} with {|S| \ll_\epsilon 1} and {\rho \gg_\epsilon 1} such that

\displaystyle  \int_G \int_G f(x) f(x+r) f(x+2r) \nu_{B(S,\rho)}*\nu_{B(S,\rho)}(r)\ d\mu(x) d\mu(r) \ \ \ \ \ (3)

\displaystyle  \geq (\int_G f\ d\mu)^3 - O(\epsilon)

where {*} denotes the convolution operation

\displaystyle  f*g(x) := \int_G f(y) g(x-y)\ d\mu(y).

A variant of this result (expressed in the language of ergodic theory) appears in this paper of Bergelson, Host, and Kra; a combinatorial version of the Bergelson-Host-Kra result that is closer to Theorem 3 subsequently appeared in this paper of Ben Green and myself, but this theorem arguably appears implicitly in a much older paper of Bourgain. To see why Theorem 3 implies Theorem 1, we apply the theorem with {f := 1_A} and {\epsilon} equal to a small multiple of {\alpha^3} to conclude that there is a Bohr set {B(S,\rho)} with {|S| \ll_\alpha 1} and {\rho \gg_\alpha 1} such that

\displaystyle  \int_G \int_G 1_A(x) 1_A(x+r) 1_A(x+2r) \nu_{B(S,\rho)}*\nu_{B(S,\rho)}(r)\ d\mu(x) d\mu(r) \gg \alpha^3.

But from (2) we have the pointwise bound {\nu_{B(S,\rho)}*\nu_{B(S,\rho)} \ll_\alpha 1}, and Theorem 1 follows.

Below the fold, we give a short proof of Theorem 3, using an “energy pigeonholing” argument that essentially dates back to the 1986 paper of Bourgain mentioned previously (not to be confused with a later 1999 paper of Bourgain on Roth’s theorem that was highly influential, for instance in emphasising the importance of Bohr sets). The idea is to use the pigeonhole principle to choose the Bohr set {B(S,\rho)} to capture all the “large Fourier coefficients” of {f}, but such that a certain “dilate” of {B(S,\rho)} does not capture much more Fourier energy of {f} than {B(S,\rho)} itself. The bound (3) may then be obtained through elementary Fourier analysis, without much need to explicitly compute things like the Fourier transform of an indicator function of a Bohr set. (However, the bound obtained by this argument is going to be quite poor – of tower-exponential type.) To do this we perform a structural decomposition of {f} into “structured”, “small”, and “highly pseudorandom” components, as is common in the subject (e.g. in this previous blog post), but even though we crucially need to retain non-negativity of one of the components in this decomposition, we can avoid recourse to conditional expectation with respect to a partition (or “factor”) of the space, using instead convolution with one of the {\nu_{B(S,\rho)}} considered above to achieve a similar effect.

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A core foundation of the subject now known as arithmetic combinatorics (and particularly the subfield of additive combinatorics) are the elementary sum set estimates (sometimes known as “Ruzsa calculus”) that relate the cardinality of various sum sets

\displaystyle  A+B := \{ a+b: a \in A, b \in B \}

and difference sets

\displaystyle  A-B := \{ a-b: a \in A, b \in B \},

as well as iterated sumsets such as {3A=A+A+A}, {2A-2A=A+A-A-A}, and so forth. Here, {A, B} are finite non-empty subsets of some additive group {G = (G,+)} (classically one took {G={\bf Z}} or {G={\bf R}}, but nowadays one usually considers more general additive groups). Some basic estimates in this vein are the following:

Lemma 1 (Ruzsa covering lemma) Let {A, B} be finite non-empty subsets of {G}. Then {A} may be covered by at most {\frac{|A+B|}{|B|}} translates of {B-B}.

Proof: Consider a maximal set of disjoint translates {a+B} of {B} by elements {a \in A}. These translates have cardinality {|B|}, are disjoint, and lie in {A+B}, so there are at most {\frac{|A+B|}{|B|}} of them. By maximality, for any {a' \in A}, {a'+B} must intersect at least one of the selected {a+B}, thus {a' \in a+B-B}, and the claim follows. \Box

Lemma 2 (Ruzsa triangle inequality) Let {A,B,C} be finite non-empty subsets of {G}. Then {|A-C| \leq \frac{|A-B| |B-C|}{|B|}}.

Proof: Consider the addition map {+: (x,y) \mapsto x+y} from {(A-B) \times (B-C)} to {G}. Every element {a-c} of {A - C} has a preimage {\{ (x,y) \in (A-B) \times (B-C)\}} of this map of cardinality at least {|B|}, thanks to the obvious identity {a-c = (a-b) + (b-c)} for each {b \in B}. Since {(A-B) \times (B-C)} has cardinality {|A-B| |B-C|}, the claim follows. \Box

Such estimates (which are covered, incidentally, in Section 2 of my book with Van Vu) are particularly useful for controlling finite sets {A} of small doubling, in the sense that {|A+A| \leq K|A|} for some bounded {K}. (There are deeper theorems, most notably Freiman’s theorem, which give more control than what elementary Ruzsa calculus does, however the known bounds in the latter theorem are worse than polynomial in {K} (although it is conjectured otherwise), whereas the elementary estimates are almost all polynomial in {K}.)

However, there are some settings in which the standard sum set estimates are not quite applicable. One such setting is the continuous setting, where one is dealing with bounded open sets in an additive Lie group (e.g. {{\bf R}^n} or a torus {({\bf R}/{\bf Z})^n}) rather than a finite setting. Here, one can largely replicate the discrete sum set estimates by working with a Haar measure in place of cardinality; this is the approach taken for instance in this paper of mine. However, there is another setting, which one might dub the “discretised” setting (as opposed to the “discrete” setting or “continuous” setting), in which the sets {A} remain finite (or at least discretisable to be finite), but for which there is a certain amount of “roundoff error” coming from the discretisation. As a typical example (working now in a non-commutative multiplicative setting rather than an additive one), consider the orthogonal group {O_n({\bf R})} of orthogonal {n \times n} matrices, and let {A} be the matrices obtained by starting with all of the orthogonal matrice in {O_n({\bf R})} and rounding each coefficient of each matrix in this set to the nearest multiple of {\epsilon}, for some small {\epsilon>0}. This forms a finite set (whose cardinality grows as {\epsilon\rightarrow 0} like a certain negative power of {\epsilon}). In the limit {\epsilon \rightarrow 0}, the set {A} is not a set of small doubling in the discrete sense. However, {A \cdot A} is still close to {A} in a metric sense, being contained in the {O_n(\epsilon)}-neighbourhood of {A}. Another key example comes from graphs {\Gamma := \{ (x, f(x)): x \in G \}} of maps {f: A \rightarrow H} from a subset {A} of one additive group {G = (G,+)} to another {H = (H,+)}. If {f} is “approximately additive” in the sense that for all {x,y \in G}, {f(x+y)} is close to {f(x)+f(y)} in some metric, then {\Gamma} might not have small doubling in the discrete sense (because {f(x+y)-f(x)-f(y)} could take a large number of values), but could be considered a set of small doubling in a discretised sense.

One would like to have a sum set (or product set) theory that can handle these cases, particularly in “high-dimensional” settings in which the standard methods of passing back and forth between continuous, discrete, or discretised settings behave poorly from a quantitative point of view due to the exponentially large doubling constant of balls. One way to do this is to impose a translation invariant metric {d} on the underlying group {G = (G,+)} (reverting back to additive notation), and replace the notion of cardinality by that of metric entropy. There are a number of almost equivalent ways to define this concept:

Definition 3 Let {(X,d)} be a metric space, let {E} be a subset of {X}, and let {r>0} be a radius.

  • The packing number {N^{pack}_r(E)} is the largest number of points {x_1,\dots,x_n} one can pack inside {E} such that the balls {B(x_1,r),\dots,B(x_n,r)} are disjoint.
  • The internal covering number {N^{int}_r(E)} is the fewest number of points {x_1,\dots,x_n \in E} such that the balls {B(x_1,r),\dots,B(x_n,r)} cover {E}.
  • The external covering number {N^{ext}_r(E)} is the fewest number of points {x_1,\dots,x_n \in X} such that the balls {B(x_1,r),\dots,B(x_n,r)} cover {E}.
  • The metric entropy {N^{ent}_r(E)} is the largest number of points {x_1,\dots,x_n} one can find in {E} that are {r}-separated, thus {d(x_i,x_j) \geq r} for all {i \neq j}.

It is an easy exercise to verify the inequalities

\displaystyle  N^{ent}_{2r}(E) \leq N^{pack}_r(E) \leq N^{ext}_r(E) \leq N^{int}_r(E) \leq N^{ent}_r(E)

for any {r>0}, and that {N^*_r(E)} is non-increasing in {r} and non-decreasing in {E} for the three choices {* = pack,ext,ent} (but monotonicity in {E} can fail for {*=int}!). It turns out that the external covering number {N^{ent}_r(E)} is slightly more convenient than the other notions of metric entropy, so we will abbreviate {N_r(E) = N^{ent}_r(E)}. The cardinality {|E|} can be viewed as the limit of the entropies {N^*_r(E)} as {r \rightarrow 0}.

If we have the bounded doubling property that {B(0,2r)} is covered by {O(1)} translates of {B(0,r)} for each {r>0}, and one has a Haar measure {m} on {G} which assigns a positive finite mass to each ball, then any of the above entropies {N^*_r(E)} is comparable to {m( E + B(0,r) ) / m(B(0,r))}, as can be seen by simple volume packing arguments. Thus in the bounded doubling setting one can usually use the measure-theoretic sum set theory to derive entropy-theoretic sumset bounds (see e.g. this paper of mine for an example of this). However, it turns out that even in the absence of bounded doubling, one still has an entropy analogue of most of the elementary sum set theory, except that one has to accept some degradation in the radius parameter {r} by some absolute constant. Such losses can be acceptable in applications in which the underlying sets {A} are largely “transverse” to the balls {B(0,r)}, so that the {N_r}-entropy of {A} is largely independent of {A}; this is a situation which arises in particular in the case of graphs {\Gamma = \{ (x,f(x)): x \in G \}} discussed above, if one works with “vertical” metrics whose balls extend primarily in the vertical direction. (I hope to present a specific application of this type here in the near future.)

Henceforth we work in an additive group {G} equipped with a translation-invariant metric {d}. (One can also generalise things slightly by allowing the metric to attain the values {0} or {+\infty}, without changing much of the analysis below.) By the Heine-Borel theorem, any precompact set {E} will have finite entropy {N_r(E)} for any {r>0}. We now have analogues of the two basic Ruzsa lemmas above:

Lemma 4 (Ruzsa covering lemma) Let {A, B} be precompact non-empty subsets of {G}, and let {r>0}. Then {A} may be covered by at most {\frac{N_r(A+B)}{N_r(B)}} translates of {B-B+B(0,2r)}.

Proof: Let {a_1,\dots,a_n \in A} be a maximal set of points such that the sets {a_i + B + B(0,r)} are all disjoint. Then the sets {a_i+B} are disjoint in {A+B} and have entropy {N_r(a_i+B)=N_r(B)}, and furthermore any ball of radius {r} can intersect at most one of the {a_i+B}. We conclude that {N_r(A+B) \geq n N_r(B)}, so {n \leq \frac{N_r(A+B)}{N_r(B)}}. If {a \in A}, then {a+B+B(0,r)} must intersect one of the {a_i + B + B(0,r)}, so {a \in a_i + B-B + B(0,2r)}, and the claim follows. \Box

Lemma 5 (Ruzsa triangle inequality) Let {A,B,C} be precompact non-empty subsets of {G}, and let {r>0}. Then {N_{4r}(A-C) \leq \frac{N_r(A-B) N_r(B-C)}{N_r(B)}}.

Proof: Consider the addition map {+: (x,y) \mapsto x+y} from {(A-B) \times (B-C)} to {G}. The domain {(A-B) \times (B-C)} may be covered by {N_r(A-B) N_r(B-C)} product balls {B(x,r) \times B(y,r)}. Every element {a-c} of {A - C} has a preimage {\{ (x,y) \in (A-B) \times (B-C)\}} of this map which projects to a translate of {B}, and thus must meet at least {N_r(B)} of these product balls. However, if two elements of {A-C} are separated by a distance of at least {4r}, then no product ball can intersect both preimages. We thus see that {N_{4r}^{ent}(A-C) \leq \frac{N_r(A-B) N_r(B-C)}{N_r(A-C)}}, and the claim follows. \Box

Below the fold we will record some further metric entropy analogues of sum set estimates (basically redoing much of Chapter 2 of my book with Van Vu). Unfortunately there does not seem to be a direct way to abstractly deduce metric entropy results from their sum set analogues (basically due to the failure of a certain strong version of Freiman’s theorem, as discussed in this previous post); nevertheless, the proofs of the discrete arguments are elementary enough that they can be modified with a small amount of effort to handle the entropy case. (In fact, there should be a very general model-theoretic framework in which both the discrete and entropy arguments can be processed in a unified manner; see this paper of Hrushovski for one such framework.)

It is also likely that many of the arguments here extend to the non-commutative setting, but for simplicity we will not pursue such generalisations here.

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Emmanuel Breuillard, Ben Green, and I have just uploaded to the arXiv our survey “Small doubling in groups“, for the proceedings of the upcoming Erdos Centennial.  This is a short survey of the known results on classifying finite subsets A of an (abelian) additive group G = (G,+) or a (not necessarily abelian) multiplicative group G = (G,\cdot) that have small doubling in the sense that the sum set A+A or product set A \cdot A is small.  Such sets behave approximately like finite subgroups of G (and there is a closely related notion of an approximate group in which the analogy is even tighter) , and so this subject can be viewed as a sort of approximate version of finite group theory.  (Unfortunately, thus far the theory does not have much new to say about the classification of actual finite groups; progress has been largely made instead on classifying the (highly restricted) number of ways in which approximate groups can differ from a genuine group.)

In the classical case when G is the integers {\mathbb Z}, these sets were classified (in a qualitative sense, at least) by a celebrated theorem of Freiman, which roughly speaking says that such sets A are necessarily “commensurate” in some sense with a (generalised) arithmetic progression P of bounded rank.   There are a number of essentially equivalent ways to define what “commensurate” means here; for instance, in the original formulation of the theorem, one asks that A be a dense subset of P, but in modern formulations it is often more convenient to require instead that A be of comparable size to P and be covered by a bounded number of translates of P, or that A and P have an intersection that is of comparable size to both A and P (cf. the notion of commensurability in group theory).

Freiman’s original theorem was extended to more general abelian groups in a sequence of papers culminating in the paper of Green and Ruzsa that handled arbitrary abelian groups.   As such groups now contain non-trivial finite subgroups, the conclusion of the theorem must be  modified by allowing for “coset progressions” P+H, which can be viewed as “extensions”  of generalized arithmetic progressions P by genuine finite groups H.

The proof methods in these abelian results were Fourier-analytic in nature (except in the cases of sets of very small doubling, in which more combinatorial approaches can be applied, and there were also some geometric or combinatorial methods that gave some weaker structural results).  As such, it was a challenge to extend these results to nonabelian groups, although for various important special types of ambient group G (such as an linear group over a finite or infinite field) it turns out that one can use tools exploiting the special structure of those groups (e.g. for linear groups one would use tools from Lie theory and algebraic geometry) to obtain quite satisfactory results; see e.g. this survey of  Pyber and Szabo for the linear case.   When the ambient group G is completely arbitrary, it turns out the problem is closely related to the classical Hilbert’s fifth problem of determining the minimal requirements of a topological group in order for such groups to have Lie structure; this connection was first observed and exploited by Hrushovski, and then used by Breuillard, Green, and myself to obtain the analogue of Freiman’s theorem for an arbitrary nonabelian group.

This survey is too short to discuss in much detail the proof techniques used in these results (although the abelian case is discussed in this book of mine with Vu, and the nonabelian case discussed in this more recent book of mine), but instead focuses on the statements of the various known results, as well as some remaining open questions in the subject (in particular, there is substantial work left to be done in making the estimates more quantitative, particularly in the nonabelian setting).

We have now seen two ways to construct expander Cayley graphs {Cay(G,S)}. The first, discussed in Notes 2, is to use Cayley graphs that are projections of an infinite Cayley graph on a group with Kazhdan’s property (T). The second, discussed in Notes 3, is to combine a quasirandomness property of the group {G} with a flattening hypothesis for the random walk.

We now pursue the second approach more thoroughly. The main difficulty here is to figure out how to ensure flattening of the random walk, as it is then an easy matter to use quasirandomness to show that the random walk becomes mixing soon after it becomes flat. In the case of Selberg’s theorem, we achieved this through an explicit formula for the heat kernel on the hyperbolic plane (which is a proxy for the random walk). However, in most situations such an explicit formula is not available, and one must develop some other tool for forcing flattening, and specifically an estimate of the form

\displaystyle  \| \mu^{(n)} \|_{\ell^2(G)} \ll |G|^{-1/2+\epsilon} \ \ \ \ \ (1)

for some {n = O(\log |G|)}, where {\mu} is the uniform probability measure on the generating set {S}.

In 2006, Bourgain and Gamburd introduced a general method for achieving this goal. The intuition here is that the main obstruction that prevents a random walk from spreading out to become flat over the entire group {G} is if the random walk gets trapped in some proper subgroup {H} of {G} (or perhaps in some coset {xH} of such a subgroup), so that {\mu^{(n)}(xH)} remains large for some moderately large {n}. Note that

\displaystyle  \mu^{(2n)}(H) \geq \mu^{(n)}(H x^{-1}) \mu^{(n)}(xH) = \mu^{(n)}(xH)^2,

since {\mu^{(2n)} = \mu^{(n)} * \mu^{(n)}}, {H = (H x^{-1}) \cdot (xH)}, and {\mu^{(n)}} is symmetric. By iterating this observation, we seethat if {\mu^{(n)}(xH)} is too large (e.g. of size {|G|^{-o(1)}} for some {n} comparable to {\log |G|}), then it is not possible for the random walk {\mu^{(n)}} to converge to the uniform distribution in time {O(\log |G|)}, and so expansion does not occur.

A potentially more general obstruction of this type would be if the random walk gets trapped in (a coset of) an approximate group {H}. Recall that a {K}-approximate group is a subset {H} of a group {G} which is symmetric, contains the identity, and is such that {H \cdot H} can be covered by at most {K} left-translates (or equivalently, right-translates) of {H}. Such approximate groups were studied extensively in last quarter’s course. A similar argument to the one given previously shows (roughly speaking) that expansion cannot occur if {\mu^{(n)}(xH)} is too large for some coset {xH} of an approximate group.

It turns out that this latter observation has a converse: if a measure does not concentrate in cosets of approximate groups, then some flattening occurs. More precisely, one has the following combinatorial lemma:

Lemma 1 (Weighted Balog-Szemerédi-Gowers lemma) Let {G} be a group, let {\nu} be a finitely supported probability measure on {G} which is symmetric (thus {\nu(g)=\nu(g^{-1})} for all {g \in G}), and let {K \geq 1}. Then one of the following statements hold:

  • (i) (Flattening) One has {\| \nu * \nu \|_{\ell^2(G)} \leq \frac{1}{K} \|\nu\|_{\ell^2(G)}}.
  • (ii) (Concentration in an approximate group) There exists an {O(K^{O(1)})}-approximate group {H} in {G} with {|H| \ll K^{O(1)} / \| \nu \|_{\ell^2(G)}^2} and an element {x \in G} such that {\nu(xH) \gg K^{-O(1)}}.

This lemma is a variant of the more well-known Balog-Szemerédi-Gowers lemma in additive combinatorics due to Gowers (which roughly speaking corresponds to the case when {\mu} is the uniform distribution on some set {A}), which in turn is a polynomially quantitative version of an earlier lemma of Balog and Szemerédi. We will prove it below the fold.

The lemma is particularly useful when the group {G} in question enjoys a product theorem, which roughly speaking says that the only medium-sized approximate subgroups of {G} are trapped inside genuine proper subgroups of {G} (or, contrapositively, medium-sized sets that generate the entire group {G} cannot be approximate groups). The fact that some finite groups (and specifically, the bounded rank finite simple groups of Lie type) enjoy product theorems is a non-trivial fact, and will be discussed in later notes. For now, we simply observe that the presence of the product theorem, together with quasirandomness and a non-concentration hypothesis, can be used to demonstrate expansion:

Theorem 2 (Bourgain-Gamburd expansion machine) Suppose that {G} is a finite group, that {S \subseteq G} is a symmetric set of {k} generators, and that there are constants {0 < \kappa < 1 < \Lambda} with the following properties.

  1. (Quasirandomness). The smallest dimension of a nontrivial representation {\rho: G \rightarrow GL_d({\bf C})} of {G} is at least {|G|^{\kappa}};
  2. (Product theorem). For all {\delta > 0} there is some {\delta' = \delta'(\delta) > 0} such that the following is true. If {H \subseteq G} is a {|G|^{\delta'}}-approximate subgroup with {|G|^{\delta} \leq |H| \leq |G|^{1 - \delta}} then {H} generates a proper subgroup of {G};
  3. (Non-concentration estimate). There is some even number {n \leq \Lambda\log |G|} such that

    \displaystyle  \sup_{H < G}\mu^{(n)}(H) < |G|^{-\kappa},

    where the supremum is over all proper subgroups {H < G}.

Then {Cay(G,S)} is a two-sided {\epsilon}-expander for some {\epsilon > 0} depending only on {k,\kappa, \Lambda}, and the function {\delta'(\cdot )} (and this constant {\epsilon} is in principle computable in terms of these constants).

This criterion for expansion is implicitly contained in this paper of Bourgain and Gamburd, who used it to establish the expansion of various Cayley graphs in {SL_2(F_p)} for prime {p}. This criterion has since been applied (or modified) to obtain expansion results in many other groups, as will be discussed in later notes.

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Let {\alpha \in {\bf R}/{\bf Z}} be an element of the unit circle, let {N \geq 1}, and let {\rho > 0}. We define the (rank one) Bohr set {B_N(\alpha;\rho)} to be the set

\displaystyle B_N(\alpha;\rho) := \{ n \in {\bf Z}: -N \leq n \leq N; \|n\alpha\|_{{\bf R}/{\bf Z}} \leq \rho \}

where {\|x\|_{{\bf R}/{\bf Z}}} is the distance to the origin in the unit circle (or equivalently, the distance to the nearest integer, after lifting up to {{\bf R}}). These sets play an important role in additive combinatorics and in additive number theory. For instance, they arise naturally when applying the circle method, because Bohr sets describe the oscillation of exponential phases such as {n \mapsto e^{2\pi i n \alpha}}.

Observe that Bohr sets enjoy the doubling property

\displaystyle B_N(\alpha;\rho) + B_N(\alpha;\rho) \subset B_{2N}(\alpha;2\rho),

thus doubling the Bohr set doubles both the length parameter {N} and the radius parameter {\rho}. As such, these Bohr sets resemble two-dimensional balls (or boxes). Indeed, one can view {B_N(\alpha;\rho)} as the preimage of the two-dimensional box {[-1,1] \times [-\rho,\rho] \subset {\bf R} \times {\bf R}/{\bf Z}} under the homomorphism {n \mapsto (n/N, \alpha n \hbox{ mod } 1)}.

Another class of finite set with two-dimensional behaviour is the class of (rank two) generalised arithmetic progressions

\displaystyle P( a_1,a_2; N_1,N_2 ) := \{ n_1 a_1 + n_2 a_2: n_1,n_2 \in {\bf Z}; |n_1| \leq N_1, |n_2| \leq N_2 \}

with {a_1,a_2 \in {\bf Z}} and {N_1,N_2 > 0} Indeed, we have

\displaystyle P( a_1,a_2; N_1,N_2 ) + P( a_1,a_2; N_1,N_2 ) \subset P( a_1,a_2; 2N_1, 2N_2 )

and so we see, as with the Bohr set, that doubling the generalised arithmetic progressions doubles the two defining parameters of that progression.

More generally, there is an analogy between rank {r} Bohr sets

\displaystyle B_N(\alpha_1,\ldots,\alpha_r; \rho_1,\ldots,\rho_r) := \{ n \in {\bf Z}: -N \leq n \leq N; \|n\alpha_i\|_{{\bf R}/{\bf Z}} \leq \rho_i

\displaystyle \hbox{ for all } 1 \leq i \leq r \}

and the rank {r+1} generalised arithmetic progressions

\displaystyle P( a_1,\ldots,a_{r+1}; N_1,\ldots,N_{r+1} ) := \{ n_1 a_1 + \ldots + n_{r+1} a_{r+1}:

\displaystyle n_1,\ldots,n_{r+1} \in {\bf Z}; |n_i| \leq N_i \hbox{ for all } 1 \leq i \leq r+1 \}.

One of the aims of additive combinatorics is to formalise analogies such as the one given above. By using some arguments from the geometry of numbers, for instance, one can show that for any rank {r} Bohr set {B_N(\alpha_1,\ldots,\alpha_r;\rho_1,\ldots,\rho_r)}, there is a rank {r+1} generalised arithmetic progression {P(a_1,\ldots,a_{r+1}; N_1,\ldots,N_{r+1})} for which one has the containments

\displaystyle B_N(\alpha_1,\ldots,\alpha_r;\epsilon \rho_1,\ldots,\epsilon \rho_r) \subset P(a_1,\ldots,a_{r+1}; N_1,\ldots,N_{r+1})

\displaystyle \subset B_N(\alpha_1,\ldots,\alpha_r;\rho_1,\ldots,\rho_r)

for some explicit {\epsilon>0} depending only on {r} (in fact one can take {\epsilon = (r+1)^{-2(r+1)}}); this is (a slight modification of) Lemma 4.22 of my book with Van Vu.

In the special case when {r=1}, one can make a significantly more detailed description of the link between rank one Bohr sets and rank two generalised arithmetic progressions, by using the classical theory of continued fractions, which among other things gives a fairly precise formula for the generators {a_1,a_2} and lengths {N_1,N_2} of the generalised arithmetic progression associated to a rank one Bohr set {B_N(\alpha;\rho)}. While this connection is already implicit in the continued fraction literature (for instance, in the classic text of Hardy and Wright), I thought it would be a good exercise to work it out explicitly and write it up, which I will do below the fold.

It is unfortunate that the theory of continued fractions is restricted to the rank one setting (it relies very heavily on the total ordering of one-dimensional sets such as {{\bf Z}} or {{\bf R}}). A higher rank version of the theory could potentially help with questions such as the Littlewood conjecture, which remains open despite a substantial amount of effort and partial progress on the problem. At the end of this post I discuss how one can use the rank one theory to rephrase the Littlewood conjecture as a conjecture about a doubly indexed family of rank four progressions, which can be used to heuristically justify why this conjecture should be true, but does not otherwise seem to shed much light on the problem.

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