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A few days ago, Endre Szemerédi was awarded the 2012 Abel prize “for his fundamental contributions to discrete mathematics and theoretical computer science, and in recognition of the profound and lasting impact of these contributions on additive number theory and ergodic theory.” The full citation for the prize may be found here, and the written notes for a talk given by Tim Gowers on Endre’s work at the announcement may be found here (and video of the talk can be found here).

As I was on the Abel prize committee this year, I won’t comment further on the prize, but will instead focus on what is arguably Endre’s most well known result, namely Szemerédi’s theorem on arithmetic progressions:

Theorem 1 (Szemerédi’s theorem)Let be a set of integers of positive upper density, thus , where . Then contains an arithmetic progression of length for any .

Szemerédi’s original proof of this theorem is a remarkably intricate piece of combinatorial reasoning. Most proofs of theorems in mathematics – even long and difficult ones – generally come with a reasonably compact “high-level” overview, in which the proof is (conceptually, at least) broken down into simpler pieces. There may well be technical difficulties in formulating and then proving each of the component pieces, and then in fitting the pieces together, but usually the “big picture” is reasonably clear. To give just one example, the overall strategy of Perelman’s proof of the Poincaré conjecture can be briefly summarised as follows: to show that a simply connected three-dimensional manifold is homeomorphic to a sphere, place a Riemannian metric on it and perform Ricci flow, excising any singularities that arise by surgery, until the entire manifold becomes extinct. By reversing the flow and analysing the surgeries performed, obtain enough control on the topology of the original manifold to establish that it is a topological sphere.

In contrast, the pieces of Szemerédi’s proof are highly interlocking, particularly with regard to all the epsilon-type parameters involved; it takes quite a bit of notational setup and foundational lemmas before the key steps of the proof can even be stated, let alone proved. Szemerédi’s original paper contains a logical diagram of the proof (reproduced in Gowers’ recent talk) which already gives a fair indication of this interlocking structure. (Many years ago I tried to present the proof, but I was unable to find much of a simplification, and my exposition is probably not that much clearer than the original text.) Even the use of nonstandard analysis, which is often helpful in cleaning up armies of epsilons, turns out to be a bit tricky to apply here. (In typical applications of nonstandard analysis, one can get by with a single nonstandard universe, constructed as an ultrapower of the standard universe; but to correctly model all the epsilons occuring in Szemerédi’s argument, one needs to repeatedly perform the ultrapower construction to obtain a (finite) sequence of increasingly nonstandard (and increasingly saturated) universes, each one containing unbounded quantities that are far larger than any quantity that appears in the preceding universe, as discussed at the end of this previous blog post. This sequence of universes does end up concealing all the epsilons, but it is not so clear that this is a net gain in clarity for the proof; I may return to the nonstandard presentation of Szemeredi’s argument at some future juncture.)

Instead of trying to describe the entire argument here, I thought I would instead show some key components of it, with only the slightest hint as to how to assemble the components together to form the whole proof. In particular, I would like to show how two particular ingredients in the proof – namely van der Waerden’s theorem and the Szemerédi regularity lemma – become useful. For reasons that will hopefully become clearer later, it is convenient not only to work with ordinary progressions , but also progressions of progressions , progressions of progressions of progressions, and so forth. (In additive combinatorics, these objects are known as *generalised arithmetic progressions* of rank one, two, three, etc., and play a central role in the subject, although the way they are used in Szemerédi’s proof is somewhat different from the way that they are normally used in additive combinatorics.) Very roughly speaking, Szemerédi’s proof begins by building an enormous generalised arithmetic progression of high rank containing many elements of the set (arranged in a “near-maximal-density” configuration), and then steadily prunes this progression to improve the combinatorial properties of the configuration, until one ends up with a single rank one progression of length that consists entirely of elements of .

To illustrate some of the basic ideas, let us first consider a situation in which we have located a progression of progressions of length , with each progression , being quite long, and containing a near-maximal amount of elements of , thus

where is the “maximal density” of along arithmetic progressions. (There are a lot of subtleties in the argument about exactly how good the error terms are in various approximations, but we will ignore these issues for the sake of this discussion and just use the imprecise symbols such as instead.) By hypothesis, is positive. The objective is then to locate a progression in , with each in for . It may help to view the progression of progressions as a tall thin rectangle .

If we write for , then the problem is equivalent to finding a (possibly degenerate) arithmetic progression , with each in .

By hypothesis, we know already that each set has density about in :

Let us now make a “weakly mixing” assumption on the , which roughly speaking asserts that

for “most” subsets of of density of a certain form to be specified shortly. This is a plausible type of assumption if one believes to behave like a random set, and if the sets are constructed “independently” of the in some sense. Of course, we do not expect such an assumption to be valid all of the time, but we will postpone consideration of this point until later. Let us now see how this sort of weakly mixing hypothesis could help one count progressions of the desired form.

We will inductively consider the following (nonrigorously defined) sequence of claims for each :

- : For most choices of , there are arithmetic progressions in with the specified choice of , such that for all .

(Actually, to avoid boundary issues one should restrict to lie in the middle third of , rather than near the edges, but let us ignore this minor technical detail.) The quantity is natural here, given that there are arithmetic progressions in that pass through in the position, and that each one ought to have a probability of or so that the events simultaneously hold.) If one has the claim , then by selecting a typical in , we obtain a progression with for all , as required. (In fact, we obtain about such progressions by this method.)

We can heuristically justify the claims by induction on . For , the claims are clear just from direct counting of progressions (as long as we keep away from the edges of ). Now suppose that , and the claims have already been proven. For any and for most , we have from hypothesis that there are progressions in through with . Let be the set of all the values of attained by these progressions, then . Invoking the weak mixing hypothesis, we (heuristically, at least) conclude that for most choices of , we have

which then gives the desired claim .

The observant reader will note that we only needed the claim in the case for the above argument, but for technical reasons, the full proof requires one to work with more general values of (also the claim needs to be replaced by a more complicated version of itself, but let’s ignore this for sake of discussion).

We now return to the question of how to justify the weak mixing hypothesis (2). For a single block of , one can easily concoct a scenario in which this hypothesis fails, by choosing to overlap with too strongly, or to be too disjoint from . However, one can do better if one can select from a long progression of blocks. The starting point is the following simple double counting observation that gives the right upper bound:

Proposition 2 (Single upper bound)Let be a progression of progressions for some large . Suppose that for each , the set has density in (i.e. (1) holds). Let be a subset of of density . Then (if is large enough) one can find an such that

*Proof:* The key is the double counting identity

Because has maximal density and is large, we have

for each , and thus

The claim then follows from the pigeonhole principle.

Now suppose we want to obtain weak mixing not just for a single set , but for a small number of such sets, i.e. we wish to find an for which

for all , where is the density of in . The above proposition gives, for each , a choice of for which (3) holds, but it could be a different for each , and so it is not immediately obvious how to use Proposition 2 to find an for which (3) holds *simultaneously* for all . However, it turns out that the van der Waerden theorem is the perfect tool for this amplification:

Proposition 3 (Multiple upper bound)Let be a progression of progressions for some large . Suppose that for each , the set has density in (i.e. (1) holds). For each , let be a subset of of density . Then (if is large enough depending on ) one can find an such thatsimultaneously for all .

*Proof:* Suppose that the claim failed (for some suitably large ). Then, for each , there exists such that

This can be viewed as a colouring of the interval by colours. If we take large compared to , van der Waerden’s theorem allows us to then find a long subprogression of which is monochromatic, so that is constant on this progression. But then this will furnish a counterexample to Proposition 2.

One nice thing about this proposition is that the upper bounds can be automatically upgraded to an asymptotic:

Proposition 4 (Multiple mixing)Let be a progression of progressions for some large . Suppose that for each , the set has density in (i.e. (1) holds). For each , let be a subset of of density . Then (if is large enough depending on ) one can find an such thatsimultaneously for all .

*Proof:* By applying the previous proposition to the collection of sets and their complements (thus replacing with , one can find an for which

and

which gives the claim.

However, this improvement of Proposition 2 turns out to not be strong enough for applications. The reason is that the number of sets for which mixing is established is too small compared with the length of the progression one has to use in order to obtain that mixing. However, thanks to the magic of the Szemerédi regularity lemma, one can amplify the above proposition even further, to allow for a huge number of to be mixed (at the cost of excluding a small fraction of exceptions):

Proposition 5 (Really multiple mixing)Let be a progression of progressions for some large . Suppose that for each , the set has density in (i.e. (1) holds). For each in some (large) finite set , let be a subset of of density . Then (if is large enough, butnotdependent on the size of ) one can find an such thatsimultaneously for almost all .

*Proof:* We build a bipartite graph connecting the progression to the finite set by placing an edge between an element and an element whenever . The number can then be interpreted as the degree of in this graph, while the number is the number of neighbours of that land in .

We now apply the regularity lemma to this graph . Roughly speaking, what this lemma does is to partition and into almost equally sized cells and such that for most pairs of cells, the graph resembles a random bipartite graph of some density between these two cells. The key point is that the number of cells here is bounded uniformly in the size of and . As a consequence of this lemma, one can show that for most vertices in a typical cell , the number is approximately equal to

and the number is approximately equal to

The point here is that the different statistics are now controlled by a mere statistics (this is not unlike the use of principal component analysis in statistics, incidentally, but that is another story). Now, we invoke Proposition 4 to find an for which

simultaneously for all , and the claim follows.

This proposition now suggests a way forward to establish the type of mixing properties (2) needed for the preceding attempt at proving Szemerédi’s theorem to actually work. Whereas in that attempt, we were working with a single progression of progressions of progressions containing a near-maximal density of elements of , we will now have to work with a *family* of such progression of progressions, where ranges over some suitably large parameter set. Furthermore, in order to invoke Proposition 5, this family must be “well-arranged” in some arithmetic sense; in particular, for a given , it should be possible to find many reasonably large subfamilies of this family for which the terms of the progression of progressions in this subfamily are themselves in arithmetic progression. (Also, for technical reasons having to do with the fact that the sets in Proposition 5 are not allowed to depend on , one also needs the progressions for any given to be “similar” in the sense that they intersect in the same fashion (thus the sets as varies need to be translates of each other).) If one has this sort of family, then Proposition 5 allows us to “spend” some of the degrees of freedom of the parameter set in order to gain good mixing properties for at least one of the sets in the progression of progressions.

Of course, we still have to figure out how to get such large families of well-arranged progressions of progressions. Szemerédi’s solution was to begin by working with generalised progressions of a much larger rank than the rank progressions considered here; roughly speaking, to prove Szemerédi’s theorem for length progressions, one has to consider generalised progressions of rank as high as . It is possible by a reasonably straightforward (though somewhat delicate) “density increment argument” to locate a huge generalised progression of this rank which is “saturated” by in a certain rather technical sense (related to the concept of “near maximal density” used previously). Then, by another reasonably elementary argument, it is possible to locate inside a suitable large generalised progression of some rank , a family of large generalised progressions of rank which inherit many of the good properties of the original generalised progression, and which have the arithmetic structure needed for Proposition 5 to be applicable, at least for one value of . (But getting this sort of property for *all* values of simultaneously is tricky, and requires many careful iterations of the above scheme; there is also the problem that by obtaining good behaviour for one index , one may lose good behaviour at previous indices, leading to a sort of “Tower of Hanoi” situation which may help explain the exponential factor in the rank that is ultimately needed. It is an extremely delicate argument; all the parameters and definitions have to be set very precisely in order for the argument to work at all, and it is really quite remarkable that Endre was able to see it through to the end.)

This week I am in San Diego for the annual joint mathematics meeting of the American Mathematical Society and the Mathematical Association of America. I am giving two talks here. One is a lecture (for the AMS “Current Events” Bulletin) on recent developments (by Martel-Merle, Merle-Raphael, and others) on stability of solitons; I will post on that lecture at some point in the near future, once the survey paper associated to that lecture is finalised.

The other, which I am presenting here, is an address on “structure and randomness in the prime numbers“. Of course, I’ve talked about this general topic many times before, (e.g. at my Simons lecture at MIT, my Milliman lecture at U. Washington, and my Science Research Colloquium at UCLA), and I have given similar talks to the one here – which focuses on my original 2004 paper with Ben Green on long arithmetic progressions in the primes – about a dozen or so times. As such, this particular talk has probably run its course, and so I am “retiring” it by posting it here.

p.s. At this meeting, Endre Szemerédi was awarded the 2008 Steele prize for a seminal contribution to research, for his landmark paper establishing what is now known as Szemerédi’s theorem, which underlies the result I discuss in this talk. This prize is richly deserved – congratulations Endre! [The AMS and MAA also awarded prizes to several dozen other mathematicians, including many mentioned previously on this blog; rather than list them all here, let me just point you to their prize booklet.]

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