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The equidistribution theorem asserts that if is an irrational phase, then the sequence is equidistributed on the unit circle, or equivalently that

for any continuous (or equivalently, for any smooth) function . By approximating uniformly by a Fourier series, this claim is equivalent to that of showing that

for any non-zero integer (where ), which is easily verified from the irrationality of and the geometric series formula. Conversely, if is rational, then clearly fails to go to zero when is a multiple of the denominator of .

One can then ask for more quantitative information about the decay of exponential sums of , or more generally on exponential sums of the form for an arithmetic progression (in this post all progressions are understood to be finite) and a polynomial . It will be convenient to phrase such information in the form of an *inverse theorem*, describing those phases for which the exponential sum is large. Indeed, we have

Lemma 1 (Geometric series formula, inverse form)Let be an arithmetic progression of length at most for some , and let be a linear polynomial for some . Iffor some , then there exists a subprogression of of size such that varies by at most on (that is to say, lies in a subinterval of of length at most ).

*Proof:* By a linear change of variable we may assume that is of the form for some . We may of course assume that is non-zero in , so that ( denotes the distance from to the nearest integer). From the geometric series formula we see that

and so . Setting for some sufficiently small absolute constant , we obtain the claim.

Thus, in order for a linear phase to fail to be equidistributed on some long progression , must in fact be almost constant on large piece of .

As is well known, this phenomenon generalises to higher order polynomials. To achieve this, we need two elementary additional lemmas. The first relates the exponential sums of to the exponential sums of its “first derivatives” .

Lemma 2 (Van der Corput lemma, inverse form)Let be an arithmetic progression of length at most , and let be an arbitrary function such that

for some . Then, for integers , there exists a subprogression of , of the same spacing as , such that

*Proof:* Squaring (1), we see that

We write and conclude that

where is a subprogression of of the same spacing. Since , we conclude that

for values of (this can be seen, much like the pigeonhole principle, by arguing via contradiction for a suitable choice of implied constants). The claim follows.

The second lemma (which we recycle from this previous blog post) is a variant of the equidistribution theorem.

Lemma 3 (Vinogradov lemma)Let be an interval for some , and let be such that for at least values of , for some . Then eitheror

or else there is a natural number such that

*Proof:* We may assume that and , since we are done otherwise. Then there are at least two with , and by the pigeonhole principle we can find in with and . By the triangle inequality, we conclude that there exists at least one natural number for which

We take to be minimal amongst all such natural numbers, then we see that there exists coprime to and such that

If then we are done, so suppose that . Suppose that are elements of such that and . Writing for some , we have

By hypothesis, ; note that as and we also have . This implies that and thus . We then have

We conclude that for fixed with , there are at most elements of such that . Iterating this with a greedy algorithm, we see that the number of with is at most ; since , this implies that

and the claim follows.

Now we can quickly obtain a higher degree version of Lemma 1:

Proposition 4 (Weyl exponential sum estimate, inverse form)Let be an arithmetic progression of length at most for some , and let be a polynomial of some degree at most . Iffor some , then there exists a subprogression of with such that varies by at most on .

*Proof:* We induct on . The cases are immediate from Lemma 1. Now suppose that , and that the claim had already been proven for . To simplify the notation we allow implied constants to depend on . Let the hypotheses be as in the proposition. Clearly cannot exceed . By shrinking as necessary we may assume that for some sufficiently small constant depending on .

By rescaling we may assume . By Lemma 3, we see that for choices of such that

for some interval . We write , then is a polynomial of degree at most with leading coefficient . We conclude from induction hypothesis that for each such , there exists a natural number such that , by double-counting, this implies that there are integers in the interval such that . Applying Lemma 3, we conclude that either , or that

In the former case the claim is trivial (just take to be a point), so we may assume that we are in the latter case.

We partition into arithmetic progressions of spacing and length comparable to for some large depending on to be chosen later. By hypothesis, we have

so by the pigeonhole principle, we have

for at least one such progression . On this progression, we may use the binomial theorem and (4) to write as a polynomial in of degree at most , plus an error of size . We thus can write for for some polynomial of degree at most . By the triangle inequality, we thus have (for large enough) that

and hence by induction hypothesis we may find a subprogression of of size such that varies by most on , and thus (for large enough again) that varies by at most on , and the claim follows.

This gives the following corollary (also given as Exercise 16 in this previous blog post):

Corollary 5 (Weyl exponential sum estimate, inverse form II)Let be a discrete interval for some , and let polynomial of some degree at most for some . Iffor some , then there is a natural number such that for all .

One can obtain much better exponents here using Vinogradov’s mean value theorem; see Theorem 1.6 this paper of Wooley. (Thanks to Mariusz Mirek for this reference.) However, this weaker result already suffices for many applications, and does not need any result as deep as the mean value theorem.

*Proof:* To simplify notation we allow implied constants to depend on . As before, we may assume that for some small constant depending only on . We may also assume that for some large , as the claim is trivial otherwise (set ).

Applying Proposition 4, we can find a natural number and an arithmetic subprogression of such that and such that varies by at most on . Writing for some interval of length and some , we conclude that the polynomial varies by at most on . Taking order differences, we conclude that the coefficient of this polynomial is ; by the binomial theorem, this implies that differs by at most on from a polynomial of degree at most . Iterating this, we conclude that the coefficient of is for , and the claim then follows by inverting the change of variables (and replacing with a larger quantity such as as necessary).

For future reference we also record a higher degree version of the Vinogradov lemma.

Lemma 6 (Polynomial Vinogradov lemma)Let be a discrete interval for some , and let be a polynomial of degree at most for some such that for at least values of , for some . Then either

or else there is a natural number such that

for all .

*Proof:* We induct on . For this follows from Lemma 3 (noting that if then ), so suppose that and that the claim is already proven for . We now allow all implied constants to depend on .

For each , let denote the number of such that . By hypothesis, , and clearly , so we must have for choices of . For each such , we then have for choices of , so by induction hypothesis, either (5) or (6) holds, or else for choices of , there is a natural number such that

for , where are the coefficients of the degree polynomial . We may of course assume it is the latter which holds. By the pigeonhole principle we may take to be independent of .

Since , we have

for choices of , so by Lemma 3, either (5) or (6) holds, or else (after increasing as necessary) we have

We can again assume it is the latter that holds. This implies that modulo , so that

for choices of . Arguing as before and iterating, we obtain the claim.

The above results also extend to higher dimensions. Here is the higher dimensional version of Proposition 4:

Proposition 7 (Multidimensional Weyl exponential sum estimate, inverse form)Let and , and let be arithmetic progressions of length at most for each . Let be a polynomial of degrees at most in each of the variables separately. Iffor some , then there exists a subprogression of with for each such that varies by at most on .

A much more general statement, in which the polynomial phase is replaced by a nilsequence, and in which one does not necessarily assume the exponential sum is small, is given in Theorem 8.6 of this paper of Ben Green and myself, but it involves far more notation to even state properly.

*Proof:* We induct on . The case was established in Proposition 5, so we assume that and that the claim has already been proven for . To simplify notation we allow all implied constants to depend on . We may assume that for some small depending only on .

By a linear change of variables, we may assume that for all .

We write . First suppose that . Then by the pigeonhole principle we can find such that

and the claim then follows from the induction hypothesis. Thus we may assume that for some large depending only on . Similarly we may assume that for all .

By the triangle inequality, we have

The inner sum is , and the outer sum has terms. Thus, for choices of , one has

for some polynomials of degrees at most in the variables . For each obeying (7), we apply Corollary 5 to conclude that there exists a natural number such that

for (the claim also holds for but we discard it as being trivial). By the pigeonhole principle, there thus exists a natural number such that

for all and for choices of . If we write

where is a polynomial of degrees at most , then for choices of we then have

Applying Lemma 6 in the and the largeness hypotheses on the (and also the assumption that ) we conclude (after enlarging as necessary, and pigeonholing to keep independent of ) that

for all (note that we now include that case, which is no longer trivial) and for choices of . Iterating this, we eventually conclude (after enlarging as necessary) that

whenever for , with nonzero. Permuting the indices, and observing that the claim is trivial for , we in fact obtain (8) for all , at which point the claim easily follows by taking for each .

An inspection of the proof of the above result (or alternatively, by combining the above result again with many applications of Lemma 6) reveals the following general form of Proposition 4, which was posed as Exercise 17 in this previous blog post, but had a slight misprint in it (it did not properly treat the possibility that some of the could be small) and was a bit trickier to prove than anticipated (in fact, the reason for this post was that I was asked to supply a more detailed solution for this exercise):

Proposition 8 (Multidimensional Weyl exponential sum estimate, inverse form, II)Let be an natural number, and for each , let be a discrete interval for some . Letbe a polynomial in variables of multidegrees for some . If

for some , or else there is a natural number such that

Again, the factor of is natural in this bound. In the case, the option (10) may be deleted since (11) trivially holds in this case, but this simplification is no longer available for since one needs (10) to hold for *all* (not just one ) to make (11) completely trivial. Indeed, the above proposition fails for if one removes (10) completely, as can be seen for instance by inspecting the exponential sum , which has size comparable to regardless of how irrational is.

In Notes 5, we saw that the Gowers uniformity norms on vector spaces in high characteristic were controlled by classical polynomial phases .

Now we study the analogous situation on cyclic groups . Here, there is an unexpected surprise: the polynomial phases (classical or otherwise) are no longer sufficient to control the Gowers norms once exceeds . To resolve this problem, one must enlarge the space of polynomials to a larger class. It turns out that there are at least three closely related options for this class: the *local polynomials*, the *bracket polynomials*, and the *nilsequences*. Each of the three classes has its own strengths and weaknesses, but in my opinion the nilsequences seem to be the most natural class, due to the rich algebraic and dynamical structure coming from the nilpotent Lie group undergirding such sequences. For reasons of space we shall focus primarily on the nilsequence viewpoint here.

Traditionally, nilsequences have been defined in terms of linear orbits on nilmanifolds ; however, in recent years it has been realised that it is convenient for technical reasons (particularly for the quantitative “single-scale” theory) to generalise this setup to that of *polynomial* orbits , and this is the perspective we will take here.

A polynomial phase on a finite abelian group is formed by starting with a polynomial to the unit circle, and then composing it with the exponential function . To create a nilsequence , we generalise this construction by starting with a polynomial into a *nilmanifold* , and then composing this with a Lipschitz function . (The Lipschitz regularity class is convenient for minor technical reasons, but one could also use other regularity classes here if desired.) These classes of sequences certainly include the polynomial phases, but are somewhat more general; for instance, they *almost* include *bracket polynomial* phases such as . (The “almost” here is because the relevant functions involved are only piecewise Lipschitz rather than Lipschitz, but this is primarily a technical issue and one should view bracket polynomial phases as “morally” being nilsequences.)

In these notes we set out the basic theory for these nilsequences, including their equidistribution theory (which generalises the equidistribution theory of polynomial flows on tori from Notes 1) and show that they are indeed obstructions to the Gowers norm being small. This leads to the *inverse conjecture for the Gowers norms* that shows that the Gowers norms on cyclic groups are indeed controlled by these sequences.

In the previous lectures, we have focused mostly on the equidistribution or linear patterns on a subset of the integers , and in particular on intervals . The integers are of course a very important domain to study in additive combinatorics; but there are also other fundamental model examples of domains to study. One of these is that of a vector space over a finite field of prime order. Such domains are of interest in computer science (particularly when ) and also in number theory; but they also serve as an important simplified “dyadic model” for the integers. See this survey article of Green for further discussion of this point.

The additive combinatorics of the integers , and of vector spaces over finite fields, are analogous, but not quite identical. For instance, the analogue of an arithmetic progression in is a subspace of . In many cases, the finite field theory is a little bit simpler than the integer theory; for instance, subspaces are closed under addition, whereas arithmetic progressions are only “almost” closed under addition in various senses. (For instance, is closed under addition approximately half of the time.) However, there are some ways in which the integers are better behaved. For instance, because the integers can be generated by a single generator, a homomorphism from to some other group can be described by a single group element : . However, to specify a homomorphism from a vector space to one would need to specify one group element for each dimension of . Thus we see that there is a tradeoff when passing from (or ) to a vector space model; one gains a bounded torsion property, at the expense of conceding the bounded generation property. (Of course, if one wants to deal with arbitrarily large domains, one has to concede one or the other; the only additive groups that have both bounded torsion and boundedly many generators, are bounded.)

The starting point for this course (Notes 1) was the study of equidistribution of polynomials from the integers to the unit circle. We now turn to the parallel theory of equidistribution of polynomials from vector spaces over finite fields to the unit circle. Actually, for simplicity we will mostly focus on the *classical* case, when the polynomials in fact take values in the roots of unity (where is the characteristic of the field ). As it turns out, the non-classical case is also of importance (particularly in low characteristic), but the theory is more difficult; see these notes for some further discussion.

(Linear) Fourier analysis can be viewed as a tool to study an arbitrary function on (say) the integers , by looking at how such a function correlates with *linear phases* such as , where is the fundamental character, and is a frequency. These correlations control a number of expressions relating to , such as the expected behaviour of on arithmetic progressions of length three.

In this course we will be studying higher-order correlations, such as the correlation of with quadratic phases such as , as these will control the expected behaviour of on more complex patterns, such as arithmetic progressions of length four. In order to do this, we must first understand the behaviour of exponential sums such as

Such sums are closely related to the distribution of expressions such as in the unit circle , as varies from to . More generally, one is interested in the distribution of polynomials of one or more variables taking values in a torus ; for instance, one might be interested in the distribution of the quadruplet as both vary from to . Roughly speaking, once we understand these types of distributions, then the general machinery of quadratic Fourier analysis will then allow us to understand the distribution of the quadruplet for more general classes of functions ; this can lead for instance to an understanding of the distribution of arithmetic progressions of length in the primes, if is somehow related to the primes.

More generally, to find arithmetic progressions such as in a set , it would suffice to understand the equidistribution of the quadruplet in as and vary. This is the starting point for the fundamental connection between *combinatorics* (and more specifically, the task of finding patterns inside sets) and *dynamics* (and more specifically, the theory of equidistribution and recurrence in measure-preserving dynamical systems, which is a subfield of ergodic theory). This connection was explored in one of my previous classes; it will also be important in this course (particularly as a source of motivation), but the primary focus will be on finitary, and Fourier-based, methods.

The theory of equidistribution of polynomial orbits was developed in the linear case by Dirichlet and Kronecker, and in the polynomial case by Weyl. There are two regimes of interest; the (qualitative) *asymptotic regime* in which the scale parameter is sent to infinity, and the (quantitative) *single-scale regime* in which is kept fixed (but large). Traditionally, it is the asymptotic regime which is studied, which connects the subject to other asymptotic fields of mathematics, such as dynamical systems and ergodic theory. However, for many applications (such as the study of the primes), it is the single-scale regime which is of greater importance. The two regimes are not directly equivalent, but are closely related: the single-scale theory can be usually used to derive analogous results in the asymptotic regime, and conversely the arguments in the asymptotic regime can serve as a simplified model to show the way to proceed in the single-scale regime. The analogy between the two can be made tighter by introducing the (qualitative) *ultralimit regime*, which is formally equivalent to the single-scale regime (except for the fact that explicitly quantitative bounds are abandoned in the ultralimit), but resembles the asymptotic regime quite closely.

We will view the equidistribution theory of polynomial orbits as a special case of Ratner’s theorem, which we will study in more generality later in this course.

For the finitary portion of the course, we will be using *asymptotic notation*: , , or denotes the bound for some absolute constant , and if we need to depend on additional parameters then we will indicate this by subscripts, e.g. means that for some depending only on . In the ultralimit theory we will use an analogue of asymptotic notation, which we will review later in these notes.

Today, Prof. Margulis continued his lecture series, focusing on two specific examples of homogeneous dynamics applications to number theory, namely counting lattice points on algebraic varieties, and quantitative versions of the Oppenheim conjecture. (Due to lack of time, the third application mentioned in the previous lecture, namely metric theory of Diophantine approximation, was not covered.)

The final distinguished lecture series for the academic year here at UCLA is being given this week by Gregory Margulis, who is giving three lectures on “homogeneous dynamics and number theory”. In his first lecture, Prof. Margulis surveyed some classical problems in number theory that turn out, rather surprisingly, to have more or less equivalent counterparts in homogeneous dynamics – the theory of dynamical systems on homogeneous spaces .

As usual, any errors in this post are due to my transcription of the talk.

This week I was in Columbus, Ohio, attending a conference on equidistribution on manifolds. I talked about my recent paper with Ben Green on the quantitative behaviour of polynomial sequences in nilmanifolds, which I have blogged about previously. During my talk (and inspired by the immediately preceding talk of Vitaly Bergelson), I stated explicitly for the first time a generalisation of the van der Corput trick which morally underlies our paper, though it is somewhat buried there as we specialised it to our application at hand (and also had to deal with various quantitative issues that made the presentation more complicated). After the talk, several people asked me for a more precise statement of this trick, so I am presenting it here, and as an application reproving an old theorem of Leon Green that gives a necessary and sufficient condition as to whether a linear sequence on a nilmanifold is equidistributed, which generalises the famous theorem of Weyl on equidistribution of polynomials.

UPDATE, Feb 2013: It has been pointed out to me by Pavel Zorin that this argument does not fully recover the theorem of Leon Green; to cover all cases, one needs the more complicated van der Corput argument in our paper.

Ben Green and I have just uploaded our joint paper, “The distribution of polynomials over finite fields, with applications to the Gowers norms“, to the arXiv, and submitted to Contributions to Discrete Mathematics. This paper, which we first announced at the recent FOCS meeting, and then gave an update on two weeks ago on this blog, is now in final form. It is being made available simultaneously with a closely related paper of Lovett, Meshulam, and Samorodnitsky.

In the previous post on this topic, I focused on the negative results in the paper, and in particular the fact that the inverse conjecture for the Gowers norm fails for certain degrees in low characteristic. Today, I’d like to focus instead on the positive results, which assert that for polynomials in many variables over finite fields whose degree is less than the characteristic of the field, one has a satisfactory theory for the distribution of these polynomials. Very roughly speaking, the main technical results are:

- A
*regularity lemma*: Any polynomial can be expressed as a combination of a bounded number of other polynomials which are*regular*, in the sense that no non-trivial linear combination of these polynomials can be expressed efficiently in terms of lower degree polynomials. - A
*counting lemma*: A regular collection of polynomials behaves as if the polynomials were selected randomly. In particular, the polynomials are jointly equidistributed.

Ben Green and I have just uploaded our paper “The quantitative behaviour of polynomial orbits on nilmanifolds” to the arXiv (and shortly to be submitted to a journal, once a companion paper is finished). This paper grew out of our efforts to prove the *Möbius and Nilsequences conjecture* MN(s) from our earlier paper, which has applications to counting various linear patterns in primes (Dickson’s conjecture). These efforts were successful – as the companion paper will reveal – but it turned out that in order to establish this number-theoretic conjecture, we had to first establish a purely dynamical quantitative result about polynomial sequences in nilmanifolds, very much in the spirit of the celebrated theorems of Marina Ratner on unipotent flows; I plan to discuss her theorems in more detail in a followup post to this one.In this post I will not discuss the number-theoretic applications or the connections with Ratner’s theorem, and instead describe our result from a slightly different viewpoint, starting from some very simple examples and gradually moving to the general situation considered in our paper.

To begin with, consider a infinite linear sequence in the unit circle , where . (One can think of this sequence as the orbit of under the action of the shift operator on the unit circle.) This sequence can do one of two things:

- If is rational, then the sequence is periodic and thus only takes on finitely many values.
- If is irrational, then the sequence is dense in . In fact, it is not just dense, it is equidistributed, or equivalently that
for all continuous functions . This statement is known as the equidistribution theorem.

We thus see that infinite linear sequences exhibit a sharp dichotomy in behaviour between periodicity and equidistribution; intermediate scenarios, such as concentration on a fractal set (such as a Cantor set), do not occur with linear sequences. This dichotomy between structure and randomness is in stark contrast to exponential sequences such as , which can exhibit an extremely wide spectrum of behaviours. For instance, the question of whether is equidistributed mod 1 is an old unsolved problem, equivalent to asking whether is normal base 10.

Intermediate between linear sequences and exponential sequences are *polynomial sequences* , where P is a polynomial with coefficients in . A famous theorem of Weyl asserts that infinite polynomial sequences enjoy the same dichotomy as their linear counterparts, namely that they are either periodic (which occurs when all non-constant coefficients are rational) or equidistributed (which occurs when at least one non-constant coefficient is irrational). Thus for instance the fractional parts of are equidistributed modulo 1. This theorem is proven by Fourier analysis combined with non-trivial bounds on Weyl sums.

For our applications, we are interested in strengthening these results in two directions. Firstly, we wish to generalise from polynomial sequences in the circle to polynomial sequences in other homogeneous spaces, in particular nilmanifolds. Secondly, we need quantitative equidistribution results for finite orbits rather than qualitative equidistribution for infinite orbits .

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