Arithmetic Progressions - Almost Periodicity

The purpose of this repository is to digitise some mathematical definitions, theorem statements and theorem proofs. Digitisation, or formalisation, is a process where the source material, typically a mathematical textbook or a pdf file or website or video, is transformed into definitions in a target system consisting of a computer implementation of a logical theory (such as set theory or type theory).

The source

The definitions, theorems and proofs in this repository are taken from the exposition of Bloom and Sisask on the Kelley-Meka bound on Roth numbers 2302.07211.

The main result is that there is some constant c > 0 such that, if A ⊆ {1, ..., N} contains no non-trivial arithmetic progression of length 3, then |A| ≤ N/exp(c * (log n)^(1/12))) for some constant c > 0. This is an amazing improvement over previous bounds, which were all of the form N/(log n)^c for some constant c.

The target

The formal system which we are using as a target system is Lean's dependent type theory. Lean is a project being developed at AWS and Microsoft Research by Leonardo de Moura and his team.

At this stage, mathlib4 contains all the background material that we need. However, not all quality-of-life improvements that we know from Lean 3 have made it to Lean 4 (yet) and Lean 4 suffers from its own problems that currently make it unsuitable for new formalisation of research-level mathematics. As a consequence, we are sticking to Lean 3 until the project is complete. Then we will port it to Lean 4 in order to upstream our basic results.

Content of this project

This project currently contains about 3k lines of Lean code about the discrete (difference) convolution, discrete Lp norms, discrete Fourier transform. It also contains proofs of a version of almost periodicity and of a quantitative version of the Marcinkiewicz-Zygmund inequality.

Once finished, this project will contain two main results (here R is the Roth number, the maximum size of a set without three term arithmetic progressions):

The finite field case: A proof that R(F_q^n) ≤ q ^ (n - c * n ^ (1/9)) for some constant c. This is worse than the Ellenberg-Gijswijt bound R(F_q^n) ≤ q ^ (n - c * n) which was formalised in Dahmen, Hölzl, Lewis. The goal here is therefore not to improve on the existing bound but instead demonstrate the probability and Fourier analysis techniques, whereas Ellenberg-Gijswijt used the polynomial method.
The integer case: A proof that R(n) ≤ N/exp(c * (log n)^(1/9))), using the same techniques as in the finite field case, except for the fact that we now use Bohr sets instead of subspaces. This bound is a slight improvement over the Kelley-Meka bound (with 1/12 as the exponent instead of 1/9). It is due to Bloom and Sisask.

Code organisation

The Lean code is contained in the directory src/. The subdirectories are:

mathlib: Material missing from existing mathlib developments
prereqs: New developments to be integrated to mathlib
physics: The physical (as opposed to Fourier space) proof steps that are shared between the finite field cases and integer case
finite_field: The proof steps specific to the finite field case
integer: The proof steps specific to the integer case

See the next section for how to browse it.

What next?

Almost periodicity is nowadays a standard tool in additive combinatorics. The version we formalised is sufficient for many applications. In particular, it gives one of the best known bounds on Freiman's theorem. If some time is left at the end of the project, we might tackle Freiman's theorem.

The discrete convolution/Lp norm/Fourier transform material belongs in mathlib and we hope to PR it there once the transition to Lean 4 has completed. Almost periodicity should similarly be upstreamed to mathlib given the numerous applications. The rest of the material might forever live in this repository.

How to browse this repository

Blueprint

Below we explain how to engage with the Lean code directly. We also provide a blueprint including a dependency graph of the main ingredients in the repository. This blueprint is developed in sync with the Lean formalization, and will hence see frequent updates during the length of the project. More information on building the blueprint locally is given below.

Getting the project

At the moment, the recommended way of browsing this repository, is by using a Lean development environment. Crucially, this will allow you to introspect Lean's "Goal state" during proofs, and easily jump to definitions or otherwise follow paths through the code.

We are looking into ways to setup an online interactive website that will provide the same experience without the hassle of installing a complete Lean development environment.

For the time being: please use the installation instructions to install Lean and a supporting toolchain. After that, download and open a copy of the repository by executing the following command in a terminal:

leanproject get YaelDillies/LeanAPAP
code LeanAPAP

For detailed instructions on how to work with Lean projects, see this. The script scripts/get-cache.sh in the folder LeanAPAP will download the olean files created by our continuous integration. This will save you some time by not having to run leanproject build.

Reading the project

With the project opened in VScode, you are all set to start exploring the code. There are two pieces of functionality that help a lot when browsing through Lean code:

"Go to definition": If you right-click on a name of a definition or lemma (such as dconv or Lpnorm), then you can choose "Go to definition" from the menu, and you will be taken to the relevant location in the source files. This also works by Ctrl-clicking on the name.
"Goal view": in the event that you would like to read a proof, you can step through the proof line-by-line, and see the internals of Lean's "brain" in the Goal window. If the Goal window is not open, you can open it by clicking on one of the icons in the top right hand corner.

Building the blueprint locally

To build the web version of the blueprint locally, you need a working LaTeX installation. Furthermore, you need some dependencies. Under Linux, you should be able to get the prepackaged ones with something like:

sudo apt install graphviz libgraphviz-dev libjpeg-dev pandoc
pip3 install invoke

Under Mac OS, you should be able to get these with:

brew install graphviz pandoc
pip3 install pygraphviz invoke

(This stackoverflow answer may help to fix an error installing pygraphviz.

A couple of dependencies must be installed from source, for now (leanblueprint is not yet released, and the released plastex is out of date):

cd .. # go to a folder where you are happy to clone git repos
git clone https://github.com/plastex/plastex
pip3 install ./plastex
git clone https://github.com/PatrickMassot/leanblueprint
pip3 install ./leanblueprint

To actually build the blueprint, cd to the LeanAPAP folder and run

leanproject get-mathlib-cache
leanproject build
inv all html

To view the web version of the blueprint locally, run inv serve and navigate to http://localhost:8000/ in your favorite browser.

Acknowledgements

Our project builds on mathlib. We must therefore thank its numerous contributors without whom this project couldn't even have started.

Much of the project infrastructure has been adapted from

Source reference

[BS] : https://arxiv.org/abs/2302.07211

utensil/LeanAPAP