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We now give a basic application of Fourier analysis to the problem of counting additive patterns in sets, namely the following famous theorem of Roth:

Theorem 1 (Roth’s theorem) Let {A} be a subset of the integers {{\bf Z}} whose upper density

\displaystyle  \overline{\delta}(A) := \limsup_{N \rightarrow \infty} \frac{|A \cap [-N,N]|}{2N+1}

is positive. Then {A} contains infinitely many arithmetic progressions {a, a+r, a+2r} of length three, with {a \in {\bf Z}} and {r>0}.

This is the first non-trivial case of Szemerédi’s theorem, which is the same assertion but with length three arithmetic progressions replaced by progressions of length {k} for any {k}.

As it turns out, one can prove Roth’s theorem by an application of linear Fourier analysis – by comparing the set {A} (or more precisely, the indicator function {1_A} of that set, or of pieces of that set) against linear characters {n \mapsto e(\alpha n)} for various frequencies {\alpha \in {\bf R}/{\bf Z}}. There are two extreme cases to consider (which are model examples of a more general dichotomy between structure and randomness). One is when {A} is aligned up almost completely with one of these linear characters, for instance by being a Bohr set of the form

\displaystyle  \{ n \in {\bf Z}: \| \alpha n - \theta \|_{{\bf R}/{\bf Z}} < \epsilon \}

or more generally of the form

\displaystyle  \{ n \in {\bf Z}: \alpha n \in U \}

for some multi-dimensional frequency {\alpha \in {\bf T}^d} and some open set {U}. In this case, arithmetic progressions can be located using the equidistribution theory of the previous set of notes. At the other extreme, one has Fourier-uniform or Fourier-pseudorandom sets, whose correlation with any linear character is negligible. In this case, arithmetic progressions can be produced in abundance via a Fourier-analytic calculation.

To handle the general case, one must somehow synthesise together the argument that deals with the structured case with the argument that deals with the random case. There are several known ways to do this, but they can be basically classified into two general methods, namely the density increment argument (or {L^\infty} increment argument) and the energy increment argument (or {L^2} increment argument).

The idea behind the density increment argument is to introduce a dichotomy: either the object {A} being studied is pseudorandom (in which case one is done), or else one can use the theory of the structured objects to locate a sub-object of significantly higher “density” than the original object. As the density cannot exceed one, one should thus be done after a finite number of iterations of this dichotomy. This argument was introduced by Roth in his original proof of the above theorem.

The idea behind the energy increment argument is instead to decompose the original object {A} into two pieces (and, sometimes, a small additional error term): a structured component that captures all the structured objects that have significant correlation with {A}, and a pseudorandom component which has no significant correlation with any structured object. This decomposition usually proceeds by trying to maximise the “energy” (or {L^2} norm) of the structured component, or dually by trying to minimise the energy of the residual between the original object and the structured object. This argument appears for instance in the proof of the Szemerédi regularity lemma (which, not coincidentally, can also be used to prove Roth’s theorem), and is also implicit in the ergodic theory approach to such problems (through the machinery of conditional expectation relative to a factor, which is a type of orthogonal projection, the existence of which is usually established via an energy increment argument). However, one can also deploy the energy increment argument in the Fourier analytic setting, to give an alternate Fourier-analytic proof of Roth’s theorem that differs in some ways from the density increment proof.

In these notes we give both two Fourier-analytic proofs of Roth’s theorem, one proceeding via the density increment argument, and the other by the energy increment argument. As it turns out, both of these arguments extend to establish Szemerédi’s theorem, and more generally in counting other types of patterns, but this is non-trivial (requiring some sort of inverse conjecture for the Gowers uniformity norms in both cases); we will discuss this further in later notes.

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In the previous lecture, we studied the recurrence properties of compact systems, which are systems in which all measurable functions exhibit almost periodicity – they almost return completely to themselves after repeated shifting. Now, we consider the opposite extreme of mixing systems – those in which all measurable functions (of mean zero) exhibit mixing – they become orthogonal to themselves after repeated shifting. (Actually, there are two different types of mixing, strong mixing and weak mixing, depending on whether the orthogonality occurs individually or on the average; it is the latter concept which is of more importance to the task of establishing the Furstenberg recurrence theorem.)

We shall see that for weakly mixing systems, averages such as \frac{1}{N} \sum_{n=0}^{N-1} T^n f \ldots T^{(k-1)n} f can be computed very explicitly (in fact, this average converges to the constant (\int_X f\ d\mu)^{k-1}). More generally, we shall see that weakly mixing components of a system tend to average themselves out and thus become irrelevant when studying many types of ergodic averages. Our main tool here will be the humble Cauchy-Schwarz inequality, and in particular a certain consequence of it, known as the van der Corput lemma.

As one application of this theory, we will be able to establish Roth’s theorem (the k=3 case of Szemerédi’s theorem).

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Earlier this month, in the previous incarnation of this page, I posed a question which I thought was unsolved, and obtained the answer (in fact, it was solved 25 years ago) within a week. Now that this new version of the page has better feedback capability, I am now tempted to try again, since I have a large number of such questions which I would like to publicise. (Actually, I even have a secret web page full of these somewhere near my home page, though it will take a non-trivial amount of effort to find it!)

Perhaps my favourite open question is the problem on the maximal size of a cap set – a subset of {\Bbb F}^n_3 ({\Bbb F}_3 being the finite field of three elements) which contains no lines, or equivalently no non-trivial arithmetic progressions of length three. As an upper bound, one can easily modify the proof of Roth’s theorem to show that cap sets must have size O(3^n/n) (see e.g. this paper of Meshulam). This of course is better than the trivial bound of 3^n once n is large. In the converse direction, the trivial example \{0,1\}^n shows that cap sets can be as large as 2^n; the current world record is (2.2174\ldots)^n, held by Edel. The gap between these two bounds is rather enormous; I would be very interested in either an improvement of the upper bound to o(3^n/n), or an improvement of the lower bound to (3-o(1))^n. (I believe both improvements are true, though a good friend of mine disagrees about the improvement to the lower bound.)

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