Updates on learning Lean
I have now made it all the way to the end of the metric spaces chapter of the MIL, which means in principle, I should have everything I needed from the book to start the \(Out(F_n)\) project. The last exercise in the section was formalizing a proof that a complete metric space is Baire, a theorem I had not thought about since my undergraduate topology class. Writing down the proof made clear some of the pain points I'm likely to encounter when proving more substantial results.
- The
simp
tactic can only do so much: given a goal of the form \(A \cap f(n) \subseteq B \cap f(n)\), where \(f n\) is an indexed family of sets, and we have amongst our hypotheses \(A \subseteq B\), one would expectsimp
to easily close the goal. However, it made no progress, and it's unclear whether it was the indexing on the familyf
that threw it off, or it was something else. In any case, I will have a lot of 'almost' trivial goals like this, that I'd like to close automatically, but I will have to do them manually, or someone will have to write a more clever tactic. - A related pain point is more of an ergonomic one: when diving into proofs of these 'almost' trivial subgoals, I would like to fold their proofs after they're completed, so that they don't clutter the main body of the proof. This would be a fairly simple VS Code plugin, if someone choses to do it. On the other hand, perhaps this is a good thing, encouraging proof/code golfing?
- I need a good way to find lemmas related to a specific object: GPT is not doing it any more, since it can really only handle the basic lemmas. The search on Mathlib docs is a little slow, plus there does not seem to be a good way to filter down to lemmas using some sort of semantic search/filtering.
Some more tactics
field_simp
: As the name suggests, this simplifies terms living in a field to a normal form.linarith
can be provided helper lemmas to aid in simplification vialinarith [p1, p2, p3]
.trans
: Used to prove goals that are transitive relations. Given a goals \sim t
,trans u
will create two subgoalss \sim u
andu \sim t
.intros
: Repeated use ofintro
until quantifiers in goal are gone.constructor
: If the goal is structure with several components, e.g. an AND statement, it will create a goal for each field in the original goal.congr
andgcongr
: These are congruence tactics, which as far as I understand, are useful when working with group/ring/whatever isomorphisms, and arguments based on applying isomorphisms.aesop
: This is a more powerful version ofsimp
, with better proof search algorithms. An example of this is thecontinuity
tactic.choose
andchoose!
: These two tactics essentially convert a complex hypothesis asserting existence of objects satisfying certain properties to functions that construct those objects, and hypotheses that the constructed objects must satisfy.Nat.le_induction
andNat.strong_induction
: These are alternate recursors forNat
, which can be used with theinduction
tactic to start induction at a positiveNat
, or do strong induction.
Preparing for the \(\mathrm{Out}(F_n)\) project
The first step, before actually writing any code, would be to figure out what's already in mathlib that I will need.
- Groups with presentations: Presented group
- Metric spaces and covering spaces, specifically graphs: Metric spaces
- Group actions on metric spaces via isometries: Group actions by isometries
- Gromov hyperbolic groups and spaces: Search on Google and Zulip doesn't turn up much, but asking people in the community might help point to some Github repo that has bits of these implemented.
- PL structures: Unclear if this will be helpful in defining a PL structure on Outer Space.
Organizing code
- Naming conventions: Refer to this more thoroughly once I start uploading code on Github.
- Documentation conventions: Also refer to this when uploading code to Github, as well as sending PRs to mathlib.
- Library style guidelines
- Sections and variables
- Namespaces
On typeclass inference
- The syntax
[Group G]
(or similar), isn't exactly a typeclass constraint as much as it is an implicit typeclass argument. The difference between this and implicit arguments of the form{...}
is that in the[...]
case, it uses a typeclass resolution algorithm to find the right instance, but it can possibly be provided a custom instance if the right instance is not found.
Notes on learning Lean (2/?)
On how Lean gave me the hypotheses I needed to prove a correct theorem
Working my way through Mathematics in Lean, I came across an exercise that required proving the standard monomial summation identities, e.g. \(\sum_{i=1}^{n} i = \frac{n(n+1)}{2}\), and \(\sum_{i=1 …
read moreNotes on learning Lean (1/?)
Now that I am almost done with my dissertation, I have decided to switch focus and concentrate on doing math that can be formally checked by the Lean Theorem Prover. The ideal sub-field of math for me to start formalizing would be Teichüller theory, and results on mapping class groups …
read moreCounting orbit points (part 3): Asymptotics for convex-cocompact groups
In the previous post, we proved Sullivan's shadow lemma, which gave us concrete estimates for special subsets of the boundary, namely shadows. Recall that the shadow of a ball of radius \(r\) based at \(y\), with the source at \(x\), denoted by \(\mathcal{O}_r(x, y)\), is the set …
read moreCounting orbit points (part 2): Patterson-Sullivan theory
In the previous post, we saw how to get an asymptotic count of orbit points under a lattice action, i.e. a finite covolume Fuchsian group. To do so, we needed the fact that the geodesic flow on the associated quotient was mixing with respect to the Liouville measure. That …
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