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Perhaps the most important structural result about general large dense graphs is the Szemerédi regularity lemma. Here is a standard formulation of that lemma:
Lemma 1 (Szemerédi regularity lemma) Let
be a graph on
vertices, and let
. Then there exists a partition
for some
with the property that for all but at most
of the pairs
, the pair
is
-regular in the sense that
whenever
are such that
and
, and
is the edge density between
and
. Furthermore, the partition is equitable in the sense that
for all
.
There are many proofs of this lemma, which is actually not that difficult to establish; see for instance these previous blog posts for some examples. In this post I would like to record one further proof, based on the spectral decomposition of the adjacency matrix of , which is essentially due to Frieze and Kannan. (Strictly speaking, Frieze and Kannan used a variant of this argument to establish a weaker form of the regularity lemma, but it is not difficult to modify the Frieze-Kannan argument to obtain the usual form of the regularity lemma instead. Some closely related spectral regularity lemmas were also developed by Szegedy.) I found recently (while speaking at the Abel conference in honour of this year’s laureate, Endre Szemerédi) that this particular argument is not as widely known among graph theory experts as I had thought, so I thought I would record it here.
For reasons of exposition, it is convenient to first establish a slightly weaker form of the lemma, in which one drops the hypothesis of equitability (but then has to weight the cells by their magnitude when counting bad pairs):
Lemma 2 (Szemerédi regularity lemma, weakened variant) . Let
be a graph on
vertices, and let
. Then there exists a partition
for some
with the property that for all pairs
outside of an exceptional set
, one has
whenever
, for some real number
, where
is the number of edges between
and
. Furthermore, we have
Let us now prove Lemma 2. We enumerate (after relabeling) as
. The adjacency matrix
of the graph
is then a self-adjoint
matrix, and thus admits an eigenvalue decomposition
for some orthonormal basis of
and some eigenvalues
, which we arrange in decreasing order of magnitude:
We can compute the trace of as
But we also have , so
.
Let be a function (depending on
) to be chosen later, with
for all
. Applying (3) and the pigeonhole principle (or the finite convergence principle, see this blog post), we can find
such that
(Indeed, the bound on is basically
iterated
times.) We can now split
is the “structured” component
is the “small” component
is the “pseudorandom” component
We now design a vertex partition to make approximately constant on most cells. For each
, we partition
into
cells on which
(viewed as a function from
to
) only fluctuates by
, plus an exceptional cell of size
coming from the values where
is excessively large (larger than
). Combining all these partitions together, we can write
for some
, where
has cardinality at most
, and for all
, the eigenfunctions
all fluctuate by at most
. In particular, if
, then (by (4) and (6)) the entries of
fluctuate by at most
on each block
. If we let
be the mean value of these entries on
, we thus have
and
, where we view the indicator functions
as column vectors of dimension
.
Next, we observe from (3) and (7) that . If we let
be the coefficients of
, we thus have
and hence by Markov’s inequality we have
outside of an exceptional set
with
If avoids
, we thus have
, by (10) and the Cauchy-Schwarz inequality.
Finally, to control we see from (4) and (8) that
has an operator norm of at most
. In particular, we have from the Cauchy-Schwarz inequality that
.
Let be the set of all pairs
where either
,
,
, or
One easily verifies that (2) holds. If is not in
, then by summing (9), (11), (12) and using (5), we see that
. The left-hand side is just
. As
, we have
and so (since )
If we let be a sufficiently rapidly growing function of
that depends on
, the second error term in (13) can be absorbed in the first, and (1) follows. This concludes the proof of Lemma 2.
To prove Lemma 1, one argues similarly (after modifying as necessary), except that the initial partition
of
constructed above needs to be subdivided further into equitable components (of size
), plus some remainder sets which can be aggregated into an exceptional component of size
(and which can then be redistributed amongst the other components to arrive at a truly equitable partition). We omit the details.
Remark 1 It is easy to verify that
needs to be growing exponentially in
in order for the above argument to work, which leads to tower-exponential bounds in the number of cells
in the partition. It was shown by Gowers that a tower-exponential bound is actually necessary here. By varying
, one basically obtains the strong regularity lemma first established by Alon, Fischer, Krivelevich, and Szegedy; in the opposite direction, setting
essentially gives the weak regularity lemma of Frieze and Kannan.
Remark 2 If we specialise to a Cayley graph, in which
is a finite abelian group and
for some (symmetric) subset
of
, then the eigenvectors are characters, and one essentially recovers the arithmetic regularity lemma of Green, in which the vertex partition classes
are given by Bohr sets (and one can then place additional regularity properties on these Bohr sets with some additional arguments). The components
of
, representing high, medium, and low eigenvalues of
, then become a decomposition associated to high, medium, and low Fourier coefficients of
.
Remark 3 The use of spectral theory here is parallel to the use of Fourier analysis to establish results such as Roth’s theorem on arithmetic progressions of length three. In analogy with this, one could view hypergraph regularity as being a sort of “higher order spectral theory”, although this spectral perspective is not as convenient as it is in the graph case.
Ben Green, and I have just uploaded to the arXiv our paper “An arithmetic regularity lemma, an associated counting lemma, and applications“, submitted (a little behind schedule) to the 70th birthday conference proceedings for Endre Szemerédi. In this paper we describe the general-degree version of the arithmetic regularity lemma, which can be viewed as the counterpart of the Szemerédi regularity lemma, in which the object being regularised is a function on a discrete interval
rather than a graph, and the type of patterns one wishes to count are additive patterns (such as arithmetic progressions
) rather than subgraphs. Very roughly speaking, this regularity lemma asserts that all such functions can be decomposed as a degree
nilsequence (or more precisely, a variant of a nilsequence that we call an virtual irrational nilsequence), plus a small error, plus a third error which is extremely tiny in the Gowers uniformity norm
. In principle, at least, the latter two errors can be readily discarded in applications, so that the regularity lemma reduces many questions in additive combinatorics to questions concerning (virtual irrational) nilsequences. To work with these nilsequences, we also establish a arithmetic counting lemma that gives an integral formula for counting additive patterns weighted by such nilsequences.
The regularity lemma is a manifestation of the “dichotomy between structure and randomness”, as discussed for instance in my ICM article or FOCS article. In the degree case
, this result is essentially due to Green. It is powered by the inverse conjecture for the Gowers norms, which we and Tamar Ziegler have recently established (paper to be forthcoming shortly; the
case of our argument is discussed here). The counting lemma is established through the quantitative equidistribution theory of nilmanifolds, which Ben and I set out in this paper.
The regularity and counting lemmas are designed to be used together, and in the paper we give three applications of this combination. Firstly, we give a new proof of Szemerédi’s theorem, which proceeds via an energy increment argument rather than a density increment one. Secondly, we establish a conjecture of Bergelson, Host, and Kra, namely that if has density
, and
, then there exist
shifts
for which
contains at least
arithmetic progressions of length
of spacing
. (The
case of this conjecture was established earlier by Green; the
case is false, as was shown by Ruzsa in an appendix to the Bergelson-Host-Kra paper.) Thirdly, we establish a variant of a recent result of Gowers-Wolf, showing that the true complexity of a system of linear forms over
indeed matches the conjectured value predicted in their first paper.
In all three applications, the scheme of proof can be described as follows:
- Apply the arithmetic regularity lemma, and decompose a relevant function
into three pieces,
.
- The uniform part
is so tiny in the Gowers uniformity norm that its contribution can be easily dealt with by an appropriate “generalised von Neumann theorem”.
- The contribution of the (virtual, irrational) nilsequence
can be controlled using the arithmetic counting lemma.
- Finally, one needs to check that the contribution of the small error
does not overwhelm the main term
. This is the trickiest bit; one often needs to use the counting lemma again to show that one can find a set of arithmetic patterns for
that is so sufficiently “equidistributed” that it is not impacted by the small error.
To illustrate the last point, let us give the following example. Suppose we have a set of some positive density (say
) and we have managed to prove that
contains a reasonable number of arithmetic progressions of length
(say), e.g. it contains at least
such progressions. Now we perturb
by deleting a small number, say
, elements from
to create a new set
. Can we still conclude that the new set
contains any arithmetic progressions of length
?
Unfortunately, the answer could be no; conceivably, all of the arithmetic progressions in
could be wiped out by the
elements removed from
, since each such element of
could be associated with up to
(or even
) arithmetic progressions in
.
But suppose we knew that the arithmetic progressions in
were equidistributed, in the sense that each element in
belonged to the same number of such arithmetic progressions, namely
. Then each element deleted from
only removes at most
progressions, and so one can safely remove
elements from
and still retain some arithmetic progressions. The same argument works if the arithmetic progressions are only approximately equidistributed, in the sense that the number of progressions that a given element
belongs to concentrates sharply around its mean (for instance, by having a small variance), provided that the equidistribution is sufficiently strong. Fortunately, the arithmetic regularity and counting lemmas are designed to give precisely such a strong equidistribution result.
A succinct (but slightly inaccurate) summation of the regularity+counting lemma strategy would be that in order to solve a problem in additive combinatorics, it “suffices to check it for nilsequences”. But this should come with a caveat, due to the issue of the small error above; in addition to checking it for nilsequences, the answer in the nilsequence case must be sufficiently “dispersed” in a suitable sense, so that it can survive the addition of a small (but not completely negligible) perturbation.
One last “production note”. Like our previous paper with Emmanuel Breuillard, we used Subversion to write this paper, which turned out to be a significant efficiency boost as we could work on different parts of the paper simultaneously (this was particularly important this time round as the paper was somewhat lengthy and complicated, and there was a submission deadline). When doing so, we found it convenient to split the paper into a dozen or so pieces (one for each section of the paper, basically) in order to avoid conflicts, and to help coordinate the writing process. I’m also looking into git (a more advanced version control system), and am planning to use it for another of my joint projects; I hope to be able to comment on the relative strengths of these systems (and with plain old email) in the future.
This week I am at Rutgers University, giving the Lewis Memorial Lectures for this year, which are also concurrently part of a workshop in random matrices. I gave four lectures, three of which were on random matrices, and one of which was on the Szemerédi regularity lemma.
The titles, abstracts, and slides of these talks are as follows.
- Szemerédi’s lemma revisited. In this general-audience talk, I discuss the Szemerédi regularity lemma (which, roughly speaking, shows that an arbitrary large dense graph can always be viewed as the disjoint union of a bounded number of pseudorandom components), and how it has recently been reinterpreted in a more analytical (and infinitary) language using the theory of graph limits or of exchangeable measures. I also discuss arithmetic analogues of this lemma, including one which (implicitly) underlies my result with Ben Green that the primes contain arbitrarily long arithmetic progressions.
- Singularity and determinant of random matrices. Here, I present recent progress in understanding the question of how likely a random matrix (e.g. one whose entries are all +1 or -1 with equal probability) is to be invertible, as well as the related question of how large the determinant should be. The case of continuous matrix ensembles (such as the Gaussian ensemble) is well understood, but the discrete case contains some combinatorial difficulties and took longer to understand properly. In particular I present the results of Kahn-Komlós-Szemerédi and later authors showing that discrete random matrices are invertible with exponentially high probability, and also give some results for the distribution of the determinant.
- The least singular value of random matrices. A more quantitative version of the question “when is a matrix invertible?” is “what is the least singular value of that matrix”? I present here the recent results of Litvak-Pajor-Rudelson-Tomczak-Jaegermann, Rudelson, myself and Vu, and Rudelson-Vershynin on addressing this question in the discrete case. A central role is played by the inverse Littlewood-Offord theorems of additive combinatorics, which give reasonably sharp necessary conditions for a discrete random walk to concentrate in a small ball.
- The circular law. One interesting application of the above theory is to extend the circular law for the spectrum of random matrices from the continuous case to the discrete case. Previous arguments of Girko and Bai for the continuous case can be transplanted to the discrete case, but the key new ingredient needed is a least singular value bound for shifted matrices
in order to avoid the spectrum being overwhelmed by pseudospectrum. It turns out that the results of the preceding lecture are almost precisely what are needed to accomplish this.
[Update, Mar 31: first lecture slides corrected. Thanks to Yoshiyasu Ishigami for pointing out a slight inaccuracy in the text.]
I’ve just uploaded to the arXiv my joint paper with Tim Austin, “On the testability and repair of hereditary hypergraph properties“, which has been submitted to Random Structures and Algorithms. In this paper we prove some positive and negative results for the testability (and the local repairability) of various properties of directed or undirected graphs and hypergraphs, which can be either monochromatic or multicoloured.
The negative results have already been discussed in a previous posting of mine, so today I will focus on the positive results. The property testing results here are finitary results, but it turns out to be rather convenient to use a certain correspondence principle (the hypergraph version of the Furstenberg correspondence principle) to convert the question into one about exchangeable probability measures on spaces of hypergraphs (i.e. on random hypergraphs whose probability distribution is invariant under exchange of vertices). Such objects are also closely related to the”graphons” and “hypergraphons” that emerge as graph limits, as studied by Lovasz-Szegedy, Elek-Szegedy, and others. Somewhat amusingly, once one does so, it then becomes convenient to keep track of objects indexed by vertex sets and how they are exchanged via the language of category theory, and in particular using the concept of a natural transformation to describe such objects as exchangeable measures, graph colourings, and local modification rules. I will try to sketch out some of these connections, after describing the main positive results.
This post is a sequel of sorts to my earlier post on hard and soft analysis, and the finite convergence principle. Here, I want to discuss a well-known theorem in infinitary soft analysis – the Lebesgue differentiation theorem – and whether there is any meaningful finitary version of this result. Along the way, it turns out that we will uncover a simple analogue of the Szemerédi regularity lemma, for subsets of the interval rather than for graphs. (Actually, regularity lemmas seem to appear in just about any context in which fine-scaled objects can be approximated by coarse-scaled ones.) The connection between regularity lemmas and results such as the Lebesgue differentiation theorem was recently highlighted by Elek and Szegedy, while the connection between the finite convergence principle and results such as the pointwise ergodic theorem (which is a close cousin of the Lebesgue differentiation theorem) was recently detailed by Avigad, Gerhardy, and Towsner.
The Lebesgue differentiation theorem has many formulations, but we will avoid the strongest versions and just stick to the following model case for simplicity:
Lebesgue differentiation theorem. If
is Lebesgue measurable, then for almost every
we have
. Equivalently, the fundamental theorem of calculus
is true for almost every x in [0,1].
Here we use the oriented definite integral, thus . Specialising to the case where
is an indicator function, we obtain the Lebesgue density theorem as a corollary:
Lebesgue density theorem. Let
be Lebesgue measurable. Then for almost every
, we have
as
, where |A| denotes the Lebesgue measure of A.
In other words, almost all the points x of A are points of density of A, which roughly speaking means that as one passes to finer and finer scales, the immediate vicinity of x becomes increasingly saturated with A. (Points of density are like robust versions of interior points, thus the Lebesgue density theorem is an assertion that measurable sets are almost like open sets. This is Littlewood’s first principle.) One can also deduce the Lebesgue differentiation theorem back from the Lebesgue density theorem by approximating f by a finite linear combination of indicator functions; we leave this as an exercise.
In this second lecture, I wish to talk about the dichotomy between structure and randomness as it manifests itself in four closely related areas of mathematics:
- Combinatorial number theory, which seeks to find patterns in unstructured dense sets (or colourings) of integers;
- Ergodic theory (or more specifically, multiple recurrence theory), which seeks to find patterns in positive-measure sets under the action of a discrete dynamical system on probability spaces (or more specifically, measure-preserving actions of the integers
);
- Graph theory, or more specifically the portion of this theory concerned with finding patterns in large unstructured dense graphs; and
- Ergodic graph theory, which is a very new and undeveloped subject, which roughly speaking seems to be concerned with the patterns within a measure-preserving action of the infinite permutation group
, which is one of several models we have available to study infinite “limits” of graphs.
The two “discrete” (or “finitary”, or “quantitative”) fields of combinatorial number theory and graph theory happen to be related to each other, basically by using the Cayley graph construction; I will give an example of this shortly. The two “continuous” (or “infinitary”, or “qualitative”) fields of ergodic theory and ergodic graph theory are at present only related on the level of analogy and informal intuition, but hopefully some more systematic connections between them will appear soon.
On the other hand, we have some very rigorous connections between combinatorial number theory and ergodic theory, and also (more recently) between graph theory and ergodic graph theory, basically by the procedure of viewing the infinitary continuous setting as a limit of the finitary discrete setting. These two connections go by the names of the Furstenberg correspondence principle and the graph correspondence principle respectively. These principles allow one to tap the power of the infinitary world (for instance, the ability to take limits and perform completions or closures of objects) in order to establish results in the finitary world, or at least to take the intuition gained in the infinitary world and transfer it to a finitary setting. Conversely, the finitary world provides an excellent model setting to refine one’s understanding of infinitary objects, for instance by establishing quantitative analogues of “soft” results obtained in an infinitary manner. I will remark here that this best-of-both-worlds approach, borrowing from both the finitary and infinitary traditions of mathematics, was absolutely necessary for Ben Green and I in order to establish our result on long arithmetic progressions in the primes. In particular, the infinitary setting is excellent for being able to rigorously define and study concepts (such as structure or randomness) which are much “fuzzier” and harder to pin down exactly in the finitary world.
The question in extremal graph theory I wish to discuss here originates from Luca Trevisan; it shows that we still don’t know everything that we should about the “local” properties of large dense graphs.
Let be a large (undirected) graph, thus V is the vertex set with some large number n of vertices, and E is the collection of edges {x,y} connecting two vertices in the graph. We can allow the graph to have loops {x,x} if one wishes; it’s not terribly important for this question (since the number of loops is so small compared to the total number of edges), so let’s say there are no loops. We define three quantities of the graph G:
- The edge density
, defined as the number of edges in G, divided by the total number of possible edges, i.e.
;
- The triangle density
, defined as the number of triangles in G (i.e. unordered triplets {x,y,z} such that {x,y},{y,z}, {z,x} all lie in G), divided by the total number of possible triangles, namely
;
- The diamond density
, defined as the number of diamonds in G (i.e. unordered pairs { {x,y,z}, {x,y,w} } of triangles in G which share a common edge), divided by the total number of possible diamonds, namely
.

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