Join me, fellow readers, as I learn some topology by pretending I know something about it.

I recently uploaded Martin Escardó’s infinite search monad to hackage, under the name infinite-search. It is a delightful little thing to play with, and has inspired some deeper thoughts about the connection between computation and topology.

Quick refresher on topology: a topological space is a set X together with a collection of subsets of X, called the open subsets. The open subsets must include the empty set and the entirety of X itself, and must be closed under arbitrary unions and finite intersections.

So for example, you can generate the standard topology on the real line by taking all the open intervals (a,b) and closing them under arbitrary unions. So for example, the set , which is ball of radius 1/2 around every integer, is open. No solitary points are open; every point must have an arbitrarily small cloud of nearby points.

As another example, there is a topology on the natural numbers called the discrete topology, in which you consider every singleton set to be an open set and take arbitrary unions over those. In this topology, every subset of the naturals is open.

A space is compact if “every open cover has a finite subcover”. That means for any collection S of open sets whose union is the entire space, there is a finite collection whose union is also the entire space. Obviously all finite topologies are compact, because there are no infinite collections to begin with.

So the discrete topology on the naturals I talked about is not compact. Consider this cover: ; just all the singleton open sets. This obviously has no finite subcollection which also unions to the entire set of naturals (if you leave out even one of these sets it fails to be a cover).

Okay, on to computation. Roughly what Martin Escardó’s work says is that compact spaces can be exhaustively searched. That is, if S is a compact space, then given a total function p :: S -> Bool , we can either compute an element of S satisfying p or compute that there is no such element. If we could figure out a way to form topological spaces on our data types, then we can know whether they are exhaustively searchable (and construct algorithms to search them).

A domain is the set of all elements of a data type, including partial ones (with ⊥s in them). So the domain for Bool is { ⊥, True, False }, and the domain for Either () is { ⊥, Left ⊥, Left (), Right ⊥, Right () }. To construct a topology (the Scott topology?) for the type, generate it by the sets of all compatible elements with some finite element of the domain. So the open sets for our Either () would be { { Left (), Right () }, { Left () }, { Right () } }. We also toss in the empty set, since it is the union of zero of these.

Earlier we saw that the naturals were not compact. This is a good sign, because if we could exhaustively search the naturals, then we could write an algorithm to eg. decide the twin-prime conjecture. However, I will now show that the lazy naturals are compact.

The lazy naturals are the data type:

data Nat = Zero | Succ Nat

What is the set of elements of this domain, and what is the topology? The set of elements is just the regular naturals plus the special one let infinity = Succ infinity . The topology has all the regular finite naturals as singleton sets, just like the discrete topology, but it also has subsets generated by { ⊥, Succ ⊥, Succ (Succ ⊥), … }. That is, sets of all of them greater than some natural including infinity. The key is that the singleton { infinity } itself is not open, it only comes from one of these infinite sets.

Now let’s say you have a cover for Nat. It includes infinity, so it must include the a set { n | n > k for some k }. So we pick that one, and then we pick however many sets we need to cover all the naturals less than k; there will be at most k of them. So we have constructed a finite subcover.

So regular naturals are not compact, but Nat is. So regular naturals are not searchable, but Nat is. But we got Nat only by adding to the regular naturals; how did we make it searchable in the process?

There was a little snag in the definition of searchable above: we needed a total predicate. Many functions which we would not be able to search on the regular naturals would get into an infinite loop if we passed the function infinity, and that would mean the predicate is not total. Intuitively, that means we can only search on predicates which are bounded in the size of the natural; which stop looking at some point.

I conjecture that every ADT in Haskell (without strict fields) is compact. It is pretty easy to write combinators which construct Data.Searchable Sets for any ADT; whether they are proper compact Sets (i.e. the search function is in fact total) is another issue.

So that’s it. We can make a compact set of Naturals and exhaustively search them. For fun, here’s a little Haskell implementation demonstrating this:

import Data.Searchable data Nat = Zero | Succ Nat nat :: Set Nat nat = singleton Zero `union` fmap Succ nat -- decidable equality on functions from Nat! eqnatf :: (Eq a) => (Nat -> a) -> (Nat -> a) -> Bool eqnatf f g = forevery nat $

-> f n == g n

Go ahead, write a Num instance for Nat and play around. Just remember that your predicates need to halt even when given infinity, otherwise they are not total. In particular, this means that you can’t use eqnatf on Nat -> Nat, because equality on Nat itself is not total (infinity == infinity loops).