Pan-Galactic Division in Haskell

Summary: given an injective function A \times N \hookrightarrow B \times N, it is possible to constructively “divide by N” to obtain an injection A \hookrightarrow B, as shown recently by Peter Doyle and Cecil Qiu and expounded by Richard Schwartz. Their algorithm is nontrivial to come up with—this had been a longstanding open question—but it’s not too difficult to explain. I exhibit some Haskell code implementing the algorithm, and show some examples.

Introduction: division by two

Suppose someone hands you the following:

  • A Haskell function f :: (A, Bool) -> (B, Bool), where A and B are abstract types (i.e. their constructors are not exported, and you have no other functions whose types mention A or B).

  • A promise that the function f is injective, that is, no two values of (A, Bool) map to the same (B, Bool) value. (Thus (B, Bool) must contain at least as many inhabitants as (A, Bool).)

  • A list as :: [A], with a promise that it contains every value of type A exactly once, at a finite position.

Can you explicitly produce an injective function f' :: A -> B? Moreover, your answer should not depend on the order of elements in as.

It really seems like this ought to be possible. After all, if (B, Bool) has at least as many inhabitants as (A, Bool), then surely B must have at least as many inhabitants as A. But it is not enough to reason merely that some injection must exist; we have to actually construct one. This, it turns out, is tricky. As a first attempt, we might try f' a = fst (f (a, True)). That is certainly a function of type A -> B, but there is no guarantee that it is injective. There could be a1, a2 :: A which both map to the same b, that is, one maps to (b, False) and the other to (b, True). The picture below illustrates such a situation: (a1, True) and (a2, True) both map to b2. So the function f may be injective overall, but we can’t say much about f restricted to a particular Bool value.

9577e0a81dc50817

The requirement that the answer not depend on the order of as also makes things difficult. (Over in math-land, depending on a particular ordering of the elements in as would amount to the well-ordering principle, which is equivalent to the axiom of choice, which in turn implies the law of excluded middle—and as we all know, every time someone uses the law of excluded middle, a puppy dies. …I feel like I’m in one of those DirecTV commercials. “Don’t let a puppy die. Ignore the order of elements in as.”) Anyway, making use of the order of values in as, we could do something like the following:

  • For each a :: A:
    • Look at the B values generated by f (a,True) and f (a,False). (Note that there might only be one distinct such B value).
    • If neither B value has been used so far, pick the one that corresponds to (a,True), and add the other one to a queue of available B values.
    • If one is used and one unused, pick the unused one.
    • If both are used, pick the next available B value from the queue.

It is not too hard I couldn’t be bothered to show that this will always successfully result in a total function A -> B, which is injective by construction. (One has to show that there will always be an available B value in the queue when you need it.) The only problem is that the particular function we get depends on the order in which we iterate through the A values. The above example illustrates this as well: if the A values are listed in the order [a_1, a_2], then we first choose a_1 \mapsto b_2, and then a_2 \mapsto b_3. If they are listed in the other order, we end up with a_2 \mapsto b_2 and a_1 \mapsto b_1. Whichever value comes first “steals” b_2, and then the other one takes whatever is left. We’d like to avoid this sort of dependence on order. That is, we want a well-defined algorithm which will yield a total, injective function A -> B, which is canonical in the sense that the algorithm yields the same function given any permutation of as.

It is possible—you might enjoy puzzling over this a bit before reading on!

Division by N

The above example is a somewhat special case. More generally, let N = \{0, \dots, n-1\} denote a canonical finite set of size n, and let A and B be arbitrary sets. Then, given an injection f : A \times N \hookrightarrow B \times N, is it possible to effectively (that is, without excluded middle or the axiom of choice) compute an injection A \hookrightarrow B?

Translating down to the world of numbers representing set cardinalities—natural numbers if A and B are finite, or cardinal numbers in general—this just says that if an \leq bn then a \leq b. This statement about numbers is obviously true, so it would be nice if we could say something similar about sets, so that this fact about numbers and inequalities can be seen as just a “shadow” of a more general theorem about sets and injections.

As hinted in the introduction, the interesting part of this problem is really the word “effectively”. Using the Axiom of Choice/Law of Excluded Middle makes the problem a lot easier, but either fails to yield an actual function that we can compute with, instead merely guaranteeing the existence of such a function, or gives us a function that depends on a particular ordering of A.

Apparently this has been a longstanding open question, recently answered in the affirmative by Peter Doyle and Cecil Qiu in their paper Division By Four. It’s a really great paper: they give some fascinating historical context for the problem, and explain their algorithm (which is conceptually not all that difficult) using an intuitive analogy to a card game with certain rules. (It is not a “game” in the usual sense of having winners and losers, but really just an algorithm implemented with “players” and “cards”. In fact, you could get some friends together and actually perform this algorithm in parallel (if you have sufficiently nerdy friends).) Richard Schwartz’s companion article is also great fun and easy to follow (you should read it first).

A Game of Thrones Cards

Here’s a quick introduction to the way Doyle, Qiu, and Schwartz use a card game to formulate their algorithm. (Porting this framework to use “thrones” and “claimants” instead of “spots” and “cards” is left as an exercise to the reader.)

The finite set N is to be thought of as a set of suits. The set A will correspond to a set of players, and B to a set of ranks or values (for example, Ace, 2, 3, …) In that case B \times N corresponds to a deck of cards, each card having a rank and a suit; and we can think of A \times N in terms of each player having in front of them a number of “spots” or “slots”, each labelled by a suit. An injection A \times N \hookrightarrow B \times N is then a particular “deal” where one card has been dealt into each of the spots in front of the players. (There may be some cards left over in the deck, but the fact that the function is total means every spot has a card, and the fact that it is injective is encoded in the common-sense idea that a given card cannot be in two spots at once.) For example, the example function from before:

9577e0a81dc50817

corresponds to the following deal:

88e26b8c78506653

Here each column corresponds to one player’s hand, and the rows correspond to suit spots (with the spade spots on top and the heart spots beneath). We have mapped \{b_1, b_2, b_3\} to the ranks A, 2, 3, and mapped T and F to Spades and Hearts respectively. The spades are also highlighted in green, since later we will want to pay particular attention to what is happening with them. You might want to take a moment to convince yourself that the deal above really does correspond to the example function from before.

A Haskell implementation

Of course, doing everything effectively means we are really talking about computation. Doyle and Qiu do talk a bit about computation, but it’s still pretty abstract, in the sort of way that mathematicians talk about computation, so I thought it would be interesting to actually implement the algorithm in Haskell.

The algorithm “works” for infinite sets, but only (as far as I understand) if you consider some notion of transfinite recursion. It still counts as “effective” in math-land, but over here in programming-land I’d like to stick to (finitely) terminating computations, so we will stick to finite sets A and B.

First, some extensions and imports. Nothing too controversial.

> {-# LANGUAGE DataKinds                  #-}
> {-# LANGUAGE GADTs                      #-}
> {-# LANGUAGE GeneralizedNewtypeDeriving #-}
> {-# LANGUAGE KindSignatures             #-}
> {-# LANGUAGE RankNTypes                 #-}
> {-# LANGUAGE ScopedTypeVariables        #-}
> {-# LANGUAGE StandaloneDeriving         #-}
> {-# LANGUAGE TypeOperators              #-}
> 
> module PanGalacticDivision where
> 
> import           Control.Arrow (second, (&&&), (***))
> import           Data.Char
> import           Data.List     (find, findIndex, transpose)
> import           Data.Maybe
> 
> import           Diagrams.Prelude hiding (universe, value)
> import           Diagrams.Backend.Rasterific.CmdLine
> import           Graphics.SVGFonts

We’ll need some standard machinery for type-level natural numbers. Probably all this stuff is in a library somewhere but I couldn’t be bothered to find out. Pointers welcome.

> -- Standard unary natural number type
> data Nat :: * where
>   Z :: Nat
>   Suc :: Nat -> Nat
> 
> type One = Suc Z
> type Two = Suc One
> type Three = Suc Two
> type Four = Suc Three
> type Six = Suc (Suc Four)
> type Eight = Suc (Suc Six)
> type Ten = Suc (Suc Eight)
> type Thirteen = Suc (Suc (Suc Ten))
> 
> -- Singleton Nat-indexed natural numbers, to connect value-level and
> -- type-level Nats
> data SNat :: Nat -> * where
>   SZ :: SNat Z
>   SS :: Natural n => SNat n -> SNat (Suc n)
> 
> -- A class for converting type-level nats to value-level ones
> class Natural n where
>   toSNat :: SNat n
> 
> instance Natural Z where
>   toSNat = SZ
> 
> instance Natural n => Natural (Suc n) where
>   toSNat = SS toSNat
> 
> -- A function for turning explicit nat evidence into implicit
> natty :: SNat n -> (Natural n => r) -> r
> natty SZ r     = r
> natty (SS n) r = natty n r
> 
> -- The usual canonical finite type.  Fin n has exactly n
> -- (non-bottom) values.
> data Fin :: Nat -> * where
>   FZ :: Fin (Suc n)
>   FS :: Fin n -> Fin (Suc n)
> 
> finToInt :: Fin n -> Int
> finToInt FZ     = 0
> finToInt (FS n) = 1 + finToInt n
> 
> deriving instance Eq (Fin n)

Finiteness

Next, a type class to represent finiteness. For our purposes, a type a is finite if we can explicitly list its elements. For convenience we throw in decidable equality as well, since we will usually need that in conjunction. Of course, we have to be careful: although we can get a list of elements for a finite type, we don’t want to depend on the ordering. We must ensure that the output of the algorithm is independent of the order of elements.1 This is in fact true, although somewhat nontrivial to prove formally; I mention some of the intuitive ideas behind the proof below.

While we are at it, we give Finite instances for Fin n and for products of finite types.

> class Eq a => Finite a where
>   universe :: [a]
> 
> instance Natural n => Finite (Fin n) where
>   universe = fins toSNat
> 
> fins :: SNat n -> [Fin n]
> fins SZ     = []
> fins (SS n) = FZ : map FS (fins n)
> 
> -- The product of two finite types is finite.
> instance (Finite a, Finite b) => Finite (a,b) where
>   universe = [(a,b) | a <- universe, b <- universe]

Division, inductively

Now we come to the division algorithm proper. The idea is that panGalacticPred turns an injection A \times N \hookrightarrow B \times N into an injection A \times (N-1) \hookrightarrow B \times (N-1), and then we use induction on N to repeatedly apply panGalacticPred until we get an injection A \times 1 \hookrightarrow B \times 1.

> panGalacticDivision
>   :: forall a b n. (Finite a, Eq b)
>   => SNat n -> ((a, Fin (Suc n)) -> (b, Fin (Suc n))) -> (a -> b)

In the base case, we are given an injection A \times 1 \hookrightarrow B \times 1, so we just pass a unit value in along with the A and project out the B.

> panGalacticDivision SZ f = \a -> fst (f (a, FZ))

In the inductive case, we call panGalacticPred and recurse.

> panGalacticDivision (SS n') f = panGalacticDivision n' (panGalacticPred n' f)

Pan-Galactic Predecessor

And now for the real meat of the algorithm, the panGalacticPred function. The idea is that we swap outputs around until the function has the property that every output of the form (b,0) corresponds to an input also of the form (a,0). That is, using the card game analogy, every spade in play should be in the leftmost spot (the spades spot) of some player’s hand (some spades can also be in the deck). Then simply dropping the leftmost card in everyone’s hand (and all the spades in the deck) yields a game with no spades. That is, we will have an injection A \times \{1, \dots, n-1\} \hookrightarrow B \times \{1, \dots, n-1\}. Taking predecessors everywhere (i.e. “hearts are the new spades”) yields the desired injection A \times (N-1) \hookrightarrow B \times (N-1).

We need a Finite constraint on a so that we can enumerate all possible inputs to the function, and an Eq constraint on b so that we can compare functions for extensional equality (we iterate until reaching a fixed point). Note that whether two functions are extensionally equal does not depend on the order in which we enumerate their inputs, so far validating my claim that nothing depends on the order of elements returned by universe.

> panGalacticPred
>   :: (Finite a, Eq b, Natural n)
>   => SNat n
>   -> ((a, Fin (Suc (Suc n))) -> (b, Fin (Suc (Suc n))))
>   -> ((a, Fin (Suc n)) -> (b, Fin (Suc n)))

We construct a function f' which is related to f by a series of swaps, and has the property that it only outputs FZ when given FZ as an input. So given (a,i) we can call f' on (a, FS i) which is guaranteed to give us something of the form (b, FS j). Thus it is safe to strip off the FS and return (b, j) (though the Haskell type checker most certainly does not know this, so we just have to tell it to trust us).

> panGalacticPred n f = \(a,i) -> second unFS (f' (a, FS i))
>   where
>     unFS :: Fin (Suc n) -> Fin n
>     unFS FZ = error "impossible!"
>     unFS (FS i) = i

To construct f' we iterate a certain transformation until reaching a fixed point. For finite sets A and B this is guaranteed to terminate, though it is certainly not obvious from the Haskell code. (Encoding this in Agda so that it is accepted by the termination checker would be a fun (?) exercise.)

One round of the algorithm consists of two phases called “shape up” and “ship out” (to be described shortly).

>     oneRound = natty n $ shipOut . shapeUp
> 
>     -- iterate 'oneRound' beginning with the original function...
>     fs = iterate oneRound f
>     -- ... and stop when we reach a fixed point.
>     f' = fst . head . dropWhile (uncurry (=/=)) $ zip fs (tail fs)
>     f1 =/= f2 = all (\x -> f1 x == f2 x) universe

Encoding Card Games

Recall that a “card” is a pair of a value and a suit; we think of B as the set of values and N as the set of suits.

> type Card v s = (v, s)
> 
> value :: Card v s -> v
> value = fst
> 
> suit :: Card v s -> s
> suit = snd

Again, there are a number of players (one for each element of A), each of which has a “hand” of cards. A hand has a number of “spots” for cards, each one labelled by a different suit (which may not have any relation to the actual suit of the card in that position).

> type PlayerSpot p s = (p, s)
> type Hand v s = s -> Card v s

A “game” is an injective function from player spots to cards. Of course, the type system is not enforcing injectivity here.

> type Game p v s = PlayerSpot p s -> Card v s

Some utility functions. First, a function to project out the hand of a given player.

> hand :: p -> Game p v s -> Hand v s
> hand p g = \s -> g (p, s)

A function to swap two cards, yielding a bijection on cards.

> swap :: (Eq s, Eq v) => Card v s -> Card v s -> (Card v s -> Card v s)
> swap c1 c2 = f
>   where
>     f c
>       | c == c1   = c2
>       | c == c2   = c1
>       | otherwise = c

leftmost finds the leftmost card in a player’s hand which has a given suit.

> leftmost :: Finite s => s -> Hand v s -> Maybe s
> leftmost targetSuit h = find (\s -> suit (h s) == targetSuit) universe

Playing Rounds

playRound abstracts out a pattern that is used by both shapeUp and shipOut. The first argument is a function which, given a hand, produces a function on cards; that is, based on looking at a single hand, it decides how to swap some cards around.2 playRound then applies that function to every hand, and composes together all the resulting permutations.

Note that playRound has both Finite s and Finite p constraints, so we should think about whether the result depends on the order of elements returned by any call to universe—I claimed it does not. Finite s corresponds to suits/spots, which corresponds to N in the original problem formulation. N explicitly has a canonical ordering, so this is not a problem. The Finite p constraint, on the face of it, is more problematic. We will have to think carefully about each of the rounds implemented in terms of playRound and make sure they do not depend on the order of players. Put another way, it should be possible for all the players to take their turn simultaneously.

> playRound :: (Finite s, Finite p, Eq v) => (Hand v s -> Card v s -> Card v s) -> Game p v s -> Game p v s
> playRound withHand g = foldr (.) id swaps . g
>   where
>     swaps = map (withHand . flip hand g) players
>     players = universe

Shape Up and Ship Out

Finally, we can describe the “shape up” and “ship out” phases, beginning with “shape up”. A “bad” card is defined as one having the lowest suit; make sure every hand with any bad cards has one in the leftmost spot (by swapping the leftmost bad card with the card in the leftmost spot, if necessary).

> shapeUp :: (Finite s, Finite p, Eq v) => Game p v s -> Game p v s
> shapeUp = playRound shapeUp1
>   where
>     badSuit = head universe
>     shapeUp1 theHand =
>       case leftmost badSuit theHand of
>         Nothing      -> id
>         Just badSpot -> swap (theHand badSuit) (theHand badSpot)

And now for the “ship out” phase. Send any “bad” cards not in the leftmost spot somewhere else, by swapping with a replacement, namely, the card whose suit is the same as the suit of the spot, and whose value is the same as the value of the bad card in the leftmost spot. The point is that bad cards in the leftmost spot are OK, since we will eventually just ignore the leftmost spot. So we have to keep shipping out bad cards not in the leftmost spot until they all end up in the leftmost spot. For some intuition as to why this is guaranteed to terminate, consult Schwartz; note that columns tend to acquire more and more cards that have the same rank as a spade in the top spot (which never moves).

> shipOut :: (Finite s, Finite p, Eq v) => Game p v s -> Game p v s
> shipOut = playRound shipOutHand
>   where
>     badSuit = head universe
>     spots = universe
>     shipOutHand theHand = foldr (.) id swaps
>       where
>         swaps = map (shipOut1 . (theHand &&& id)) (drop 1 spots)
>         shipOut1 ((_,s), spot)
>           | s == badSuit = swap (theHand spot) (value (theHand badSuit), spot)
>           | otherwise    = id

And that’s it! Note that both shapeUp and shipOut are implemented by composing a bunch of swaps; in fact, in both cases, all the swaps commute, so the order in which they are composed does not matter. (For proof, see Schwartz.) Thus, the result is independent of the order of the players (i.e. the set A).

Enough code, let’s see an example! This example is taken directly from Doyle and Qiu’s paper, and the diagrams are being generated literally (literately?) by running the code in this blog post. Here’s the starting configuration:

08c40ab96ca385c0

Again, the spades are all highlighted in green. Recall that our goal is to get them all to be in the first row, but we have to do it in a completely deterministic, canonical way. After shaping up, we have:

e32d54891cc5e470

Notice how the 6, K, 5, A, and 8 of spades have all been swapped to the top of their column. However, there are still spades which are not at the top of their column (in particular the 10, 9, and J) so we are not done yet.

Now, we ship out. For example, the 10 of spades is in the diamonds position in the column with the Ace of spades, so we swap it with the Ace of diamonds. Similarly, we swap the 9 of spades with the Queen of diamonds, and the Jack of spades with the 4 of hearts.

271c3505d198b229

Shaping up does nothing at this point so we ship out again, and then continue to alternate rounds.

d2012b69fc3cc161

In the final deal above, all the spades are at the top of a column, so there is an injection from the set of all non-spade spots to the deck of cards with all spades removed. This example was, I suspect, carefully constructed so that none of the spades get swapped out into the undealt portion of the deck, and so that we end up with only spades in the top row. In general, we might end up with some non-spades also in the top row, but that’s not a problem. The point is that ignoring the top row gets rid of all the spades.

Anyway, I hope to write more about some “practical” examples and about what this has to do with combinatorial species, but this post is long enough already. Doyle and Qiu also describe a “short division” algorithm (the above is “long division”) that I hope to explore as well.

The rest of the code

For completeness, here’s the code I used to represent the example game above, and to render all the card diagrams (using diagrams 1.3).

> type Suit = Fin
> type Rank = Fin
> type Player = Fin
> 
> readRank :: SNat n -> Char -> Rank n
> readRank n c = fins n !! (fromJust $ findIndex (==c) "A23456789TJQK")
> 
> readSuit :: SNat n -> Char -> Suit n
> readSuit (SS _) 'S'                = FZ
> readSuit (SS (SS _)) 'H'           = FS FZ
> readSuit (SS (SS (SS _))) 'D'      = FS (FS FZ)
> readSuit (SS (SS (SS (SS _)))) 'C' = FS (FS (FS FZ))
> 
> readGame :: SNat a -> SNat b -> SNat n -> String -> Game (Player a) (Rank b) (Suit n)
> readGame a b n str = \(p, s) -> table !! finToInt p !! finToInt s
>   where
>     table = transpose . map (map readCard . words) . lines $ str
>     readCard [r,s] = (readRank b r, readSuit n s)
> 
> -- Example game from Doyle & Qiu
> exampleGameStr :: String
> exampleGameStr = unlines
>   [ "4D 6H QD 8D 9H QS 4C AD 6C 4S"
>   , "JH AH 9C 8H AS TC TD 5H QC JS"
>   , "KC 6S 4H 6D TS 9S JC KD 8S 8C"
>   , "5C 5D KS 5S TH JD AC QH 9D KH"
>   ]
> 
> exampleGame :: Game (Player Ten) (Rank Thirteen) (Suit Four)
> exampleGame = readGame toSNat toSNat toSNat exampleGameStr
> 
> suitSymbol :: Suit n -> String
> suitSymbol = (:[]) . ("♠♥♦♣"!!) . finToInt  -- Huzzah for Unicode
> 
> suitDia :: Suit n -> Diagram B
> suitDia = (suitDias!!) . finToInt
> 
> suitDias = map mkSuitDia (fins (toSNat :: SNat Four))
> mkSuitDia s = text' (suitSymbol s) # fc (suitColor s) # lw none
> 
> suitColor :: Suit n -> Colour Double
> suitColor n
>   | finToInt n `elem` [0,3] = black
>   | otherwise               = red
> 
> rankStr :: Rank n -> String
> rankStr n = rankStr' (finToInt n + 1)
>   where
>     rankStr' 1 = "A"
>     rankStr' i | i <= 10    = show i
>                | otherwise = ["JQK" !! (i - 11)]
> 
> text' t = stroke (textSVG' (TextOpts lin INSIDE_H KERN False 1 1) t)
> 
> renderCard :: (Rank b, Suit n) -> Diagram B
> renderCard (r, s) = mconcat
>   [ mirror label
>   , cardContent (finToInt r + 1)
>   , back
>   ]
>   where
>     cardWidth  = 2.25
>     cardHeight = 3.5
>     cardCorners = 0.1
>     mirror d = d  d # rotateBy (1/2)
>     back  = roundedRect cardWidth cardHeight cardCorners # fc white
>           # lc (case s of { FZ -> green; _ -> black })
>     label = vsep 0.1 [text' (rankStr r), text' (suitSymbol s)]
>           # scale 0.6 # fc (suitColor s) # lw none
>           # translate ((-0.9) ^& 1.5)
>     cardContent n
>       | n <= 10   = pips n
>       | otherwise = face n # fc (suitColor s) # lw none
>                            # sized (mkWidth (cardWidth * 0.6))
>     pip = suitDia s # scale 1.1
>     pips 1 = pip # scale 2
>     pips 2 = mirror (pip # up 2)
>     pips 3 = pips 2  pip
>     pips 4 = mirror (pair pip # up 2)
>     pips 5 = pips 4  pip
>     pips 6 = mirror (pair pip # up 2)  pair pip
>     pips 7 = pips 6  pip # up 1
>     pips 8 = pips 6  mirror (pip # up 1)
>     pips 9 = mirror (pair (pip # up (2/3)  pip # up 2))  pip # up (case finToInt s of {1 -> -0.1; 3 -> 0; _ -> 0.1})
>     pips 10 = mirror (pair (pip # up (2/3)  pip # up 2)  pip # up (4/3))
>     pips _ = mempty
>     up n = translateY (0.5*n)
>     pair d = hsep 0.4 [d, d] # centerX
>     face 11 = squares # frame 0.1
>     face 12 = loopyStar
>     face 13 = burst # centerXY
>     squares
>       = strokeP (mirror (square 1 # translate (0.2 ^& 0.2)))
>       # fillRule EvenOdd
>     loopyStar
>       = regPoly 7 1
>       # star (StarSkip 3)
>       # pathVertices
>       # map (cubicSpline True)
>       # mconcat
>       # fillRule EvenOdd
>     burst
>       = [(1,5), (1,-5)] # map r2 # fromOffsets
>       # iterateN 13 (rotateBy (-1/13))
>       # mconcat # glueLine
>       # strokeLoop
> 
> renderGame :: (Natural n, Natural a) => Game (Player a) (Rank b) (Suit n) -> Diagram B
> renderGame g = hsep 0.5 $ map (\p -> renderHand p $ hand p g) universe
> 
> renderHand :: Natural n => Player a -> Hand (Rank b) (Suit n) -> Diagram B
> renderHand p h = vsep 0.2 $ map (renderCard . h) universe

  1. If we could program in Homotopy Type Theory, we could make this very formal by using the notion of cardinal-finiteness developed in my dissertation (see section 2.4).

  2. In practice this function on cards will always be a permutation, though the Haskell type system is not enforcing that at all. An early version of this code used the Iso type from lens, but it wasn’t really paying its way.

Posted in haskell, math, species | Tagged , , , , , , , , , | 4 Comments

Polynomial Functors Constrained by Regular Expressions

I’ve now finished revising the paper that Dan Piponi and I had accepted to MPC 2015; you can find a PDF here:

Polynomial Functors Constrained by Regular Expressions

Here’s the 2-minute version: certain operations or restrictions on functors can be described by regular expressions, where the elements of the alphabet correspond to type arguments. The idea is to restrict to only those structures for which an inorder traversal yields a sequence of types matching the regular expression. For example, (aa)^* gives you even-size things; a^*ha^* gives you the derivative (the structure has a bunch of values of type a, a single hole of type h, and then more values of type a), and b^*ha^* the dissection.

dissected-tree

The punchline is that we show how to use the machinery of semirings, finite automata, and some basic matrix algebra to automatically derive an algebraic description of any functor constrained by any regular expression. This gives a nice unified way to view differentiation and dissection; we also draw some connections to the theory of divided differences.

I’m still open to discussion, suggestions, typo fixes, etc., though at this point they won’t make it into the proceedings. There’s certainly a lot more that could be said or ways this could be extended further.

Posted in math, writing | Tagged , , , , , , | 4 Comments

Blogging again, & some major life events

It’s been a long time since I’ve written anything here; the blog was on hold while I was finishing my PhD and on the academic job market. Now that things have settled down a bit I plan to get back to blogging.

For starters, here are a few of the major events that have happened in the meantime, that readers of this blog might care about:

  • I successfully defended my PhD dissertation in October, officially graduated in December, and got an actual diploma in the mail a few weeks ago. I’ll be back in Philadelphia for the official graduation ceremony in May.
  • I accepted a tenure-track position at Hendrix College in Conway, Arkansas, and will be moving there this summer.
  • Dan Piponi and I had a paper accepted to MPC 2015. Here’s the github repo, and I plan to post a PDF copy here soon (once I get around to incorporating feedback from the reviewers). I look forward to seeing a bunch of folks (Volk?) in Königswinter this summer; I already have my plane tickets (CIU -> DTW -> AMS -> CGN, it’s a long story).
  • Work on diagrams continues strong (no thanks to me!), and we are aiming for a big new release soon—I will certainly post about that here as well.
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Maniac week postmortem

My maniac week was a great success! First things first: here’s a time-lapse video1 (I recommend watching it at the full size, 1280×720).

Some statistics2:

  • Total hours of productive work: 55.5 (74 pings)
  • Average hours of work per day3: 11
  • Average hours of sleep per night: 7.8 (52 pings over 5 nights)4
  • Total hours not working or sleeping: 27.25 (37 pings)
  • Average hours not working per day: 5.5
  • Pages of dissertation written: 24 (157 to 181)

[I was planning to also make a visualization of my TagTime data showing when I was sleeping, working, or not-working, but putting together the video and this blog post has taken long enough already! Perhaps I’ll get around to it later.]

Overall, I would call the experiment a huge success—although as you can see, I was a full 2.5 hours per day off my target of 13.5 hours of productive work each day. What with eating, showering, making lunch, getting dinner, taking breaks (both intentional breaks as well as slacking off), and a few miscellaneous things I had to take care of like taking the car to get the tire pressure adjusted… it all adds up surprisingly fast. I think this was one of the biggest revelations for me; going into it I thought 3 hours of not-work per day was extremely generous. I now think three hours of not-work per day is probably within reach for me but would be extremely difficult, and would probably require things like planning out meals ahead of time. In any case, 55 hours of actual, focused work is still fantastic.

Some random observations/thoughts:

  • Having multiple projects to work on was really valuable; when I got tired of working on one thing I could often just switch to something else instead of taking an actual break. I can imagine this might be different if I were working on a big coding project (as most of the other maniac weeks have been). The big project would itself provide multiple different subtasks to work on, but more importantly, coding provides immediate feedback that is really addictive. Code a new feature, and you can actually run the new code! And it does something cool! That it didn’t do before! In contrast, when I write another page of my dissertation I just have… another page of my dissertation. I am, in fact, relatively excited about my dissertation, but it can’t provide that same sort of immediate reinforcing feedback, and it was difficult to keep going at times.

  • I found that having music playing really helped me get into a state of “flow”. The first few days I would play some album and then it would stop and I wouldn’t think to put on more. Later in the week I would just queue up many hours of music at a time and that worked great.

  • I was definitely feeling worn out by the end of the week—the last two days in particular, it felt a lot harder to get into a flow. I think I felt so good the first few days that I became overconfident—which is good to keep in mind if I do this again. The evening of 12 August was particularly bad; I just couldn’t focus. It might have been better in the long run to just go home and read a book or something; I’m just not sure how to tell in the moment when I should push through and when it’s better to cut my losses.

  • Blocking Facebook, turning off email notifications, etc. was really helpful. I did end up allowing myself to check email using my phone (I edited the rules a few hours before I started) and I think it was a good idea—I ended up still needing to communicate with some people, so it was very convenient and not too distracting.

  • Note there are two places on Tuesday afternoon where you can see the clock jump ahead by an hour or so; of course those are times when I turned off the recording. One corresponded to a time when I needed to read and write some sensitive emails; during the other, I was putting student pictures into an anki deck, and turned off the recording to avoid running afoul of FERPA.

That’s all I can think of for now; questions or comments, of course, are welcome.


  1. Some technical notes (don’t try this at home; see http://expost.padm.us/maniactech for some recommendations on making your own timelapse). To record and create the video I used a homegrown concoction of scrot, streamer, ImageMagick, ffmpeg, with some zsh and Haskell scripts to tie it all together, and using diagrams to generate the clock and tag displays. I took about 3GB worth of raw screenshots, and it takes probably about a half hour to process all of it into a video.

  2. These statistics are according to TagTime, i.e. gathered via random sampling, so there is a bit of inherent uncertainty. I leave it as an exercise for the reader to calculate the proper error bars on these times (given that I use a standard ping interval of 45 minutes).

  3. Computed as 74/(171 – 9) pings multiplied by 24 hours; 9 pings occurred on Sunday morning which I did not count as part of the maniac week.

  4. This is somewhat inflated by Saturday night/Sunday morning, when I both slept in and got a higher-than-average number of pings; the average excluding that night is 6.75 hours, which sounds about right.

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Readers wanted!

tl;dr: Read a draft of my thesis and send me your feedback by September 9!

Over the past year I’ve had several people say things along the lines of, “let me know if you want me to read through your thesis”. I never took them all that seriously (it’s easy to say you are willing to read a 200-page document…), but it never hurts to ask, right?

My thesis defense is scheduled for October 14, and I’m currently undertaking a massive writing/editing push to try to get as much of it wrapped up as I can before classes start on September 4. So, if there’s anyone out there actually interested in reading a draft and giving feedback, now is your chance!

The basic idea of my dissertation is to put combinatorial species and related variants (including a port of the theory to HoTT) in a common categorical framework, and then be able to use them for working with/talking about data types. If you’re brave enough to read it, you’ll find lots of category theory and type theory, and very little code—but I can promise lots of examples and pretty pictures. I’ve tried to make it somewhat self-contained, so it may be a good way to learn a bit of category theory or homotopy type theory, if you’ve been curious to learn more about those topics.

You can find the latest draft here (auto-updated every time I commit); more generally, you can find the git repo here. If you notice any typos or grammatical errors, feel free to open a pull request. For anything more substantial—thoughts on the organization, notes or questions about things you found confusing, suggestions for improvement, pointers to other references—please send me an email (first initial last name at gmail). And finally, please send me any feedback by September 9 at the latest (but the earlier the better). I need to have a final version to my committee by September 23.

Last but not least, if you’re interested to read it but don’t have the time or inclination to provide feedback on a draft, never fear—I’ll post an announcement when the final version is ready for your perusal!

Posted in category theory, combinatorics, diagrams, grad school, math, species, writing | Tagged , , , , | 15 Comments

Maniac week

Inspired by Bethany Soule (and indirectly by Nick Winter, and also by the fact that my dissertation defense and the start of the semester are looming), I am planning a “maniac week” while Joyia and Noah will be at the beach with my family (I will join them just for the weekend). The idea is to eliminate as many distractions as possible and to do a ton of focused work. Publically committing (like this) to a time frame, ground rules, and to putting up a time-lapse video of it afterwards are what actually make it work—if I don’t succeed I’ll have to admit it here on my blog; if I waste time on Facebook the whole internet will see it in the video; etc. (There’s actually no danger of wasting time on Facebook in particular since I have it blocked, but you get the idea.)

Here are the rules:

  • I will start at 6pm (or thereabouts) on Friday, August 8.
  • I will continue until 10pm on Wednesday, August 13, with the exception of the morning of Sunday, August 10 (until 2pm).
  • I will get at least 7.5 hours of sleep each night.
  • I will not eat cereal for any meal other than breakfast.
  • I will reserve 3 hours per day for things like showering, eating, and just plain resting.  Such things will be tracked by the TagTime tag “notwork”.
  • I will spend the remaining 13.5 hours per day working productively. Things that will count as productive work:
    • Working on my dissertation
    • Course prep for CS 354 (lecture and assignment planning, etc.) and CS 134 (reading through the textbook); making anki decks with names and faces for both courses
    • Updating my academic website (finish converting to Hakyll 4; add potential research and independent study topics for undergraduates)
    • Processing FogBugz tickets
    • I may work on other research or coding projects (e.g. diagrams) each day, but only after spending at least 7 hours on my dissertation.
  • I will not go on IRC at all during the week.  I will disable email notifications on my phone (but keep the phone around for TagTime), and close and block gmail in my browser.  I will also disable the program I use to check my UPenn email account.
  • For FogBugz tickets which require responding to emails, I will simply write the email in a text file and send it later.
  • I may read incoming email and write short replies on my phone, but will keep it to a bare minimum.
  • I will not read any RSS feeds during the week.  I will block feedly in my browser.
  • On August 18 I will post a time-lapse video of August 8-13.  I’ll probably also write a post-mortem blog post, if I feel like I have anything interesting to say.
  • I reserve the right to tweak these rules (by editing this post) up until August 8 at 6pm.  After that point it’s shut up and work time, and I cannot change the rules any more.

And no, I’m not crazy. You (yes, you) could do this too.

Posted in grad school, meta, writing | Tagged , , , , , | 9 Comments

Anafunctors

This is part four in a series of posts on avoiding the axiom of choice (part one, part two, part three).

In my previous post, we considered the “Axiom of Protoequivalence”—that is, the statement that every fully faithful, essentially surjective functor (i.e. every protoequivalence) is an equivalance—and I claimed that in a traditional setting this is equivalent to the axiom of choice. However, intuitively it feels like AP “ought to” be true, whereas AC must be rejected in constructive logic.

One way around this is by generalizing functors to anafunctors, which were introduced by Makkai (1996). The original paper is difficult going, since it is full of tons of detail, poorly typeset, and can only be downloaded as seven separate postscript files. There is also quite a lot of legitimate depth to the paper, which requires significant categorical sophistication (more than I possess) to fully understand. However, the basic ideas are not too hard to grok, and that’s what I will present here.

It’s important to note at the outset that anafunctors are much more than just a technical device enabling the Axiom of Protoequivalence. More generally, if everything in category theory is supposed to be done “up to isomorphism”, it is a bit suspect that functors have to be defined for objects on the nose. Anafunctors can be seen as a generalization of functors, where each object in the source category is sent not just to a single object, but to an entire isomorphism class of objects, without privileging any particular object in the class. In other words, anafunctors are functors whose “values are specified only up to unique isomorphism”.

Such functors represent a many-to-many relationship between objects of \mathbb{C} and objects of \mathbb{D}. Normal functors, as with any function, may of course map multiple objects of \mathbb{C} to the same object in \mathbb{D}. The novel aspect is the ability to have a single object of \mathbb{C} correspond to multiple objects of \mathbb{D}. The key idea is to add a class of “specifications” which mediate the relationship between objects in the source and target categories, in exactly the same way that a “junction table” must be added to support a many-to-many relationship in a database schema, as illustrated below:

On the left is a many-to-many relation between a set of shapes and a set of numbers. On the right, this relation has been mediated by a “junction table” containing a set of “specifications”—in this case, each specification is simply a pair of a shape and a number—together with two mappings (one-to-many relations) from the specifications to both of the original sets, such that a specification maps to a shape s and number n if and only if s and n were originally related.

In particular, an anafunctor F : \mathbb{C} \to \mathbb{D} is defined as follows.

  • There is a class S of specifications.
  • There are two functions \mathrm{Ob}\ \mathbb{C}  \stackrel{\overleftarrow{F}}{\longleftarrow} S  \stackrel{\overrightarrow{F}}{\longrightarrow} \mathrm{Ob}\ \mathbb{D} mapping specifications to objects of \mathbb{C} and \mathbb{D}.

S, \overleftarrow{F}, and \overrightarrow{F} together define a many-to-many relationship between objects of \mathbb{C} and objects of \mathbb{D}. D \in \mathbb{D} is called a specified value of F at C if there is some specification s \in S such that \overleftarrow{F}(s) = C and \overrightarrow{F}(s) = D, in which case we write F_s(C) = D. Moreover, D is a value of F at C (not necessarily a specified one) if there is some s for which D \cong F_s(C).

The idea now is to impose additional conditions which ensure that F “acts like” a regular functor \mathbb{C} \to \mathbb{D}.

  • Functors are defined on all objects; so we require each object of \mathbb{C} to have at least one specification s which corresponds to it—that is, \overleftarrow{F} must be surjective.
  • Functors transport morphisms as well as objects. For each s,t \in   S (the middle of the below diagram) and each f :   \overleftarrow{F}(s) \to \overleftarrow{F}(t) in \mathbb{C} (the left-hand side below), there must be a morphism F_{s,t}(f) :   \overrightarrow{F}(s) \to \overrightarrow{F}(t) in \mathbb{D} (the right-hand side):

  • Functors preserve identities: for each s \in S we should have F_{s,s}(\mathit{id}_{\overleftarrow{F}(s)}) = \mathit{id}_{\overrightarrow{F}(s)}.
  • Finally, functors preserve composition: for all s,t,u \in S (in the middle below), f : \overleftarrow{F}(s) \to \overleftarrow{F}(t), and g : \overleftarrow{F}(t) \to \overleftarrow{F}(u) (the left side below), it must be the case that F_{s,u}(f ; g) = F_{s,t}(f) ; F_{t,u}(g):

Our initial intuition was that an anafunctor should map objects of \mathbb{C} to isomorphism classes of objects in \mathbb{D}. This may not be immediately apparent from the definition, but is in fact the case. In particular, the identity morphism \mathit{id}_C maps to isomorphisms between specified values of C; that is, under the action of an anafunctor, an object C together with its identity morphism “blow up” into an isomorphism class (aka a clique). To see this, let s,t \in S be two different specifications corresponding to C, that is, \overleftarrow{F}(s) = \overleftarrow{F}(t) = C. Then by preservation of composition and identities, we have F_{s,t}(\mathit{id}_C) ; F_{t,s}(\mathit{id}_C) = F_{s,s}(\mathit{id}_C ; \mathit{id}_C) = F_{s,s}(\mathit{id}_C) = \mathit{id}_{\overrightarrow{F}(s)}, so F_{s,t}(\mathit{id}_C) and F_{t,s}(\mathit{id}_C) constitute an isomorphism between F_s(C) and F_t(C).

There is an alternative, equivalent definition of anafunctors, which is somewhat less intuitive but usually more convenient to work with: an anafunctor F : \mathbb{C} \to \mathbb{D} is a category of specifications \mathbb{S} together with a span of functors \mathbb{C} \stackrel{\overleftarrow{F}}{\longleftarrow} \mathbb{S} \stackrel{\overrightarrow{F}}{\longrightarrow} \mathbb{D} where \overleftarrow{F} is fully faithful and (strictly) surjective on objects.

Note that in this definition, \overleftarrow{F} must be strictly (as opposed to essentially) surjective on objects, that is, for every C \in \mathbb{C} there is some S \in \mathbb{S} such that \overleftarrow{F}(S) = C, rather than only requiring \overleftarrow{F}(S) \cong C. Given this strict surjectivity on objects, it is equivalent to require \overleftarrow F to be full, as in the definition above, or to be (strictly) surjective on the class of all morphisms.

We are punning on notation a bit here: in the original definition of anafunctor, S is a set and \overleftarrow{F} and \overrightarrow{F} are functions on objects, whereas in this more abstract definition \mathbb{S} is a category and \overleftarrow{F} and \overrightarrow{F} are functors. Of course, the two are closely related: given a span of functors \mathbb{C} \stackrel{\overleftarrow{F}}{\longleftarrow} \mathbb{S} \stackrel{\overrightarrow{F}}{\longrightarrow} \mathbb{D}, we may simply take the objects of \mathbb{S} as the class of specifications S, and the actions of the functors \overleftarrow{F} and \overrightarrow{F} on objects as the functions from specifications to objects of \mathbb{C} and \mathbb{D}. Conversely, given a class of specifications S and functions \overleftarrow{F} and \overrightarrow{F}, we may construct the category \mathbb{S} with \mathrm{Ob}\ \mathbb{S} = S and with morphisms \overleftarrow{F}(s) \to \overleftarrow{F}(t) in \mathbb{C} acting as morphisms s \to t in \mathbb{S}. From \mathbb{S} to \mathbb{C}, we construct the functor given by \overleftarrow{F} on objects and the identity on morphisms, and the other functor maps f : s \to t in \mathbb{S} to F_{s,t}(f) : \overrightarrow{F}(s) \to \overrightarrow{F}(t) in \mathbb{D}.

Every functor F : \mathbb{C} \to \mathbb{D} can be trivially turned into an anafunctor \mathbb{C} \stackrel{\mathit{Id}}{\longleftarrow} \mathbb{C} \stackrel{F}{\longrightarrow} \mathbb{D}. Anafunctors also compose. Given compatible anafunctors F : \mathbb{C} \stackrel{\overleftarrow F}{\longleftarrow} S \stackrel{\overrightarrow F}{\longrightarrow} \mathbb{D} and G : \mathbb{D} \stackrel{\overleftarrow G}{\longleftarrow} T \stackrel{\overrightarrow G}{\longrightarrow} \mathbb{E}, consider the action of their composite on objects: each object of \mathbb{C} may map to multiple objects of \mathbb{E}, via objects of \mathbb{D}. Each such mapping corresponds to a zig-zag path C \longleftarrow s \longrightarrow D \longleftarrow t \longrightarrow E. In order to specify such a path it suffices to give the pair (s,t), which determines C, D, and E. Note, however, that not every pair in S \times T corresponds to a valid path, but only those which agree on the middle object D \in \mathbb{D}. Thus, we may take \{ (s,t) \mid s \in S, t \in T, \overrightarrow{F}(s) = \overleftarrow{G}(t) \} as the set of specifications for the composite F ; G, with \overleftarrow{F ; G}(s,t) = \overleftarrow{F}(s) and \overrightarrow{F ; G}(s,t) = \overrightarrow{G}(t). On morphisms, (F ; G)_{(s,t),(u,v)}(f) = G_{t,v}(F_{s,u}(f)). It is not hard to check that this satisfies the anafunctor laws.

If you know what a pullback is, note that the same thing can also be defined at a higher level in terms of spans. \mathbf{Cat}, the category of all (small) categories, is complete, and in particular has pullbacks, so we may construct a new anafunctor from \mathbb{C} to \mathbb{E} by taking a pullback of \overrightarrow F and \overleftarrow G and then composing appropriately.

One can go on to define ananatural transformations between anafunctors, and show that together these constitute a 2-category \mathbf{AnaCat} which is analogous to the usual 2-category of (small) categories, functors, and natural transformations; in particular, there is a fully faithful embedding of \mathbf{Cat} into \mathbf{AnaCat}, which moreover is an equivalence if AC holds.

To work in category theory based on set theory and classical logic, while avoiding AC, one is therefore justified in “mixing and matching” functors and anafunctors as convenient, but discussing them all as if they were regular functors (except when defining a particular anafunctor). Such usage can be formalized by turning everything into an anafunctor, and translating functor operations and properties into corresponding operations and properties of anafunctors.

However, as I will argue in some future posts, there is a better solution, which is to throw out set theory as a foundation of category theory and start over with homotopy type theory. In that case, thanks to a generalized notion of equality, regular functors act like anafunctors, and in particular AP holds.

References

Makkai, Michael. 1996. “Avoiding the Axiom of Choice in General Category Theory.” Journal of Pure and Applied Algebra 108 (2). Elsevier: 109–73.

Posted in category theory, math, species | Tagged , , , , , , , , , , | 2 Comments