Enumerating Trees

Posted on December 14, 2020
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Consider the following puzzle:

Given a list of nn labels, list all the trees with those labels in order.

For instance, given the labels [1,2,3,4], the answer (for binary trees) is the following:

┌1     ┌1      ┌1     ┌1     ┌1
┤      ┤      ┌┤     ┌┤     ┌┤
│┌2    │ ┌2   ││┌2   │└2    │└2
└┤     │┌┤    │└┤    ┤     ┌┤
 │┌3   ││└3   │ └3   │┌3   │└3
 └┤    └┤     ┤      └┤    ┤
  └4    └4    └4      └4   └4

This problem (the “enumeration” problem) turns out to be quite fascinating and deep, with connections to parsing and monoids. It’s also just a classic algorithmic problem which is fun to try and solve.

The most general version of the algorithm is on forests of rose trees:

data Rose a = a :& Forest a
type Forest a = [Rose a]

It’s worth having a go at attempting it yourself, but if you’d just like to see the slick solutions the following is one I’m especially proud of:

Solution to the Enumeration Problem on Forests of Rose Trees
enumForests :: [a] -> [Forest a]
enumForests = foldrM f []
    f x xs = zipWith ((:) . (:&) x) (inits xs) (tails xs)

In the rest of this post I’ll go through the intuition behind solutions like the one above and I’ll try to elucidate some of the connections to other areas of computer science.

A First Approach: Trying to Enumerate Directly

I first came across the enumeration problem when I was writing my master’s thesis: I needed to prove (in Agda) that there were finitely many binary trees of a given size, and that I could list them (this proof was part of a larger verified solver for the countdown problem). My first few attempts were unsuccessful: the algorithm presented in the countdown paper (Hutton 2002) was not structurally recursive, and did not seem amenable to Agda-style proofs.

Instead, I looked for a type which was isomorphic to binary trees, and which might be easier to reason about. One such type is Dyck words.

Dyck Words

A “Dyck word” is a string of balanced parentheses.


It’s (apparently) well-known that these strings are isomorphic to binary trees (although the imperative descriptions of algorithms which actually computed this isomorphism addled my brain), but what made them interesting for me was that they are a flat type, structured like a linked list, and as such should be reasonably straightforward to prove to be finite.

Our first task, then, is to write down a type for Dyck words. Te following is a first possibility:

data Paren = LParen | RParen
type Dyck = [Paren]

But this type isn’t correct. It includes many values which don’t represent balanced parentheses, i.e. the expressions [LParen,RParen] :: Dyck are well-typed. To describe dyck words properly we’ll need to reach for the GADTs:

data DyckSuff (n :: Nat) :: Type where
  Done :: DyckSuff Z
  Open :: DyckSuff (S n) -> DyckSuff n
  Clos :: DyckSuff n     -> DyckSuff (S n)

type Dyck = DyckSuff Z

The first type here represents suffixes of Dyck words; a value of type DyckSuff n represents a string of parentheses which is balanced except for n extraneous closing parentheses. DyckSuff Z, then, has no extraneous closing parens, and as such is a proper Dyck word.

>>> Open $ Clos $ Open $ Clos $ Done :: Dyck

>>> Clos $ Open $ Clos $ Done :: DyckSuff (S Z)

>>> Open $ Open $ Clos $ Open $ Clos $ Clos $ Open $ Clos $ Done :: Dyck

>>> Open $ Open $ Clos $ Clos $ Open $ Clos $ Done :: Dyck

The next task is to actually enumerate these words. Here’s an O(n)O(n) algorithm which does just that:

enumDyck :: Int -> [Dyck]
enumDyck sz = go Zy sz Done []
    go, zero, left, right :: Natty n -> Int -> DyckSuff n -> [Dyck] -> [Dyck]
    go n m k = zero n m k . left n m k . right n m k

    zero Zy 0 k = (k:)
    zero _  _ _ = id
    left (Sy n) m k = go n m (Open k)
    left Zy     _ _ = id
    right _ 0 _ = id
    right n m k = go (Sy n) (m-1) (Clos k) 

>>> mapM_ print (enumDyck 3)

A variant of this function was what I needed in my thesis: I also needed to prove that it produced every possible value of the type Dyck, which was not too difficult.

The difficult part is still ahead, though: now we need to convert between this type and a binary tree.


First, for the conversion algorithms we’ll actually need another GADT:

infixr 5 :-
data Stack (a :: Type) (n :: Nat) :: Type where
  Nil  :: Stack a Z
  (:-) :: a -> Stack a n -> Stack a (S n)

The familiar length-indexed vector will be extremely useful for the next few bits of code: it will act as a stack in our stack-based algorithms. Here’s one of those algorithms now:

dyckToTree :: Dyck -> Tree
dyckToTree dy = go dy (Leaf :- Nil)
    go :: DyckSuff n -> Stack Tree (S n) -> Tree
    go (Open d) ts               = go d (Leaf :- ts)
    go (Clos d) (t1 :- t2 :- ts) = go d (t2 :*: t1 :- ts)
    go Done     (t  :- Nil)      = t

This might be familiar: it’s actually shift-reduce parsing dressed up with some types. The nice thing about it is that it’s completely total: all pattern-matches are accounted for here, and when written in Agda it’s clearly structurally terminating.

The function in the other direction is similarly simple:

treeToDyck :: Tree -> Dyck
treeToDyck t = go t Done
    go :: Tree -> DyckSuff n -> DyckSuff n
    go Leaf        = id
    go (xs :*: ys) = go xs . Open . go ys . Clos

A Compiler

Much of this stuff has been on my mind recently because of this (2020) video on the computerphile channel, in which Graham Hutton goes through using QuickCheck to test an interesting compiler. The compiler itself is explored more in depth in Bahr and Hutton (2015), where the algorithms developed are really quite similar to those that we have here.

The advantage of the code above is that it’s all total: we will never pop items off the stack that aren’t there. This is a nice addition, and it’s surprisingly simple to add: let’s see if we can add it to the compiler presented in the paper.

The first thing we need to change is we need to add a payload to our tree type: the one above is just the shape of a binary tree, but the language presented in the paper contains values.

data Expr (a :: Type) where
  Val   :: a -> Expr a
  (:+:) :: Expr a -> Expr a -> Expr a

We’ll need to change the definition of Dyck similarly:

data Code (n :: Nat) (a :: Type) :: Type where
  HALT :: Code (S Z) a
  PUSH :: a -> Code (S n) a -> Code n a
  ADD  :: Code (S n) a -> Code (S (S n)) a

After making it so that these data structures can now store contents, there are two other changes worth pointing out:

With these definitions we can actually transcribe the exec and comp functions almost verbatim (from page 11 and 12 of 2015).

exec :: Code n Int -> Stack Int (n + m) -> Stack Int (S m)
exec HALT         st              = st
exec (PUSH v is)  st              = exec is (v :- st)
exec (ADD    is) (t1 :- t2 :- st) = exec is (t2 + t1 :- st)

comp :: Expr a -> Code Z a
comp e = comp' e HALT
    comp' :: Expr a -> Code (S n) a -> Code n a
    comp' (Val     x) = PUSH x
    comp' (xs :+: ys) = comp' xs . comp' ys . ADD

Proving the Isomorphism

As I have mentioned, a big benefit of all of this stuff is that it can be translated into Agda readily. The real benefit of that is that we can show the two representations of programs are fully isomorphic. I have proven this here: the proof is surprisingly short (about 20 lines), and the rest of the code follows the Haskell stuff quite closely. I got the idea for much of the proof from this bit of code by Callan McGill (2020).

I’ll include it here as a reference.

Agda Code
open import Prelude
open import Data.Nat using (_+_)
open import Data.Vec.Iterated using (Vec; __; []; foldlN; head)

    n :

-- Binary trees: definition and associated functions

data Tree (A : Type a) : Type a where
  [_] : A  Tree A
  _*_ : Tree A  Tree A  Tree A

-- Programs: definition and associated functions

data Prog (A : Type a) : Type a where
  halt : Prog A 1
  push : A  Prog A (1 + n)  Prog A n
  pull : Prog A (1 + n)  Prog A (2 + n)

-- Conversion from a Prog to a Tree

prog→tree⊙ : Prog A n  Vec (Tree A) n  Tree A
prog→tree⊙ halt        (v ∷ [])       = v
prog→tree⊙ (push v is) st             = prog→tree⊙ is ([ v ] ∷ st)
prog→tree⊙ (pull   is) (t₁ ∷ t₂ ∷ st) = prog→tree⊙ is (t₂ * t₁ ∷ st)

prog→tree : Prog A zero  Tree A
prog→tree ds = prog→tree⊙ ds []

-- Conversion from a Tree to a Prog

tree→prog⊙ : Tree A  Prog A (suc n)  Prog A n
tree→prog⊙ [ x ]     = push x
tree→prog⊙ (xs * ys) = tree→prog⊙ xs ∘ tree→prog⊙ ys ∘ pull

tree→prog : Tree A  Prog A zero
tree→prog tr = tree→prog⊙ tr halt

-- Proof of isomorphism

tree→prog→tree⊙ : (e : Tree A) (is : Prog A (1 + n)) (st : Vec (Tree A) n) 
  prog→tree⊙ (tree→prog⊙ e is) st ≡ prog→tree⊙ is (e ∷ st)
tree→prog→tree⊙ [ x ]     is st = refl
tree→prog→tree⊙ (xs * ys) is st = tree→prog→tree⊙ xs _ st ;
                                  tree→prog→tree⊙ ys (pull is) (xs ∷ st)

tree→prog→tree : (e : Tree A)  prog→tree (tree→prog e) ≡ e
tree→prog→tree e = tree→prog→tree⊙ e halt []

prog→tree→prog⊙ : (is : Prog A n) (st : Vec (Tree A) n) 
 tree→prog (prog→tree⊙ is st) ≡ foldlN (Prog A) tree→prog⊙ is st
prog→tree→prog⊙  halt       st = refl
prog→tree→prog⊙ (push i is) st = prog→tree→prog⊙ is ([ i ] ∷ st)
prog→tree→prog⊙ (pull is) (t₁ ∷ t₂ ∷ ts) = prog→tree→prog⊙ is ((t₂ * t₁) ∷ ts)

prog→tree→prog : (is : Prog A 0)  tree→prog (prog→tree is) ≡ is
prog→tree→prog is = prog→tree→prog⊙ is []

prog-iso : Prog A zero ⇔ Tree A
prog-iso .fun = prog→tree
prog-iso .inv = tree→prog
prog-iso .rightInv = tree→prog→tree
prog-iso .leftInv  = prog→tree→prog

Folds and Whatnot

Another thing I’ll mention is that all of the exec functions presented are folds. In particular, they’re left folds. Here’s how we’d rewrite exec to make that fact clear:

foldlCode :: (∀ n. a -> b n -> b (S n))
          -> (∀ n. b (S (S n)) -> b (S n))
          -> b m
          -> Code m a -> b (S Z)
foldlCode _ _ h  HALT       = h
foldlCode p a h (PUSH x xs) = foldlCode p a (p x h) xs
foldlCode p a h (ADD    xs) = foldlCode p a (a   h) xs

shift :: Int -> Stack Int n -> Stack Int (S n)
shift x xs = x :- xs

reduce :: Stack Int (S (S n)) -> Stack Int (S n)
reduce (t1 :- t2 :- st) = t2 + t1 :- st

execFold :: Code Z Int -> Int
execFold = pop . foldlCode shift reduce Nil

I think the “foldl-from-foldr” trick could be a nice way to explain the introduction of continuations in Bahr and Hutton (2015).

Direct Enumeration

It turns out that you can follow relatively straightforward rewriting steps from the Dyck-based enumeration algorithm to get to one which avoids Dyck words entirely:

enumTrees :: [a] -> [Expr a]
enumTrees = fmap (foldl1 (flip (:+:))) . foldlM f []
    f []         v = [[Val v]]
    f [t1]       v = [[Val v, t1]]
    f (t1:t2:st) v = (Val v : t1 : t2 : st) : f ((t2 :+: t1) : st) v

Maybe in a future post I’ll go through the derivation of this algorithm.

It turns out that the Dyck-based enumeration can be applied without much difficulty to rose trees as well:

data Rose a = a :& Forest a
type Forest a = [Rose a]

dyckToForest :: Dyck -> Forest ()
dyckToForest dy = go dy ([] :- Nil)
    go :: DyckSuff n -> Stack (Forest ()) (S n) -> Forest ()
    go (Open d) ts               = go d ([] :- ts)
    go (Clos d) (t1 :- t2 :- ts) = go d ((() :& t2 : t1) :- ts)
    go Done     (t  :- Nil)      = t

forestToDyck :: Forest () -> Dyck
forestToDyck t = go t Done
    go :: Forest () -> DyckSuff n -> DyckSuff n
    go []          = id
    go ((() :& x):xs) = go x . Open . go xs . Clos

And again, following relatively mechanical derivations, we arrive at an elegant algorithm:

enumForests :: [a] -> [Forest a]
enumForests = foldrM f []
    f x xs = zipWith ((:) . (:&) x) (inits xs) (tails xs)

Related Work

While researching this post I found that enumeration of trees has been studied extensively elsewhere: see Knuth (2006), for example, or the excellent blog post by Tychonievich (2013), or the entire field of Boltzmann sampling. This post has only scratched the surface of all of that: I hope to write much more on the topic in the future.


As I mentioned, the Agda code for this stuff can be found here, I have also put all of the Haskell code in one place here.


Bahr, Patrick, and Graham Hutton. 2015. “Calculating correct compilers.” Journal of Functional Programming 25 (e14) (September). doi:10.1017/S0956796815000180. https://nottingham-repository.worktribe.com/output/761112.
Hutton, Graham. 2002. “The Countdown Problem.” J. Funct. Program. 12 (6) (November): 609–616. doi:10.1017/S0956796801004300. http://www.cs.nott.ac.uk/~pszgmh/countdown.pdf.
Knuth, Donald E. 2006. The Art of Computer Programming, Volume 4, Fascicle 4: Generating All TreesHistory of Combinatorial Generation (Art of Computer Programming). Addison-Wesley Professional. http://www.cs.utsa.edu/~wagner/knuth/fasc4a.pdf.
McGill, Callan. 2020. “Compiler correctness for addition language.” https://gist.github.com/Boarders/9d83f9cbcfaffb04cf2464588fc46df9.
Riley, Sean. 2020. “Program Correctness - Computerphile.” University of Nottingham. https://www.youtube.com/watch?v=T_IINWzQhow.
Tychonievich, Luther. 2013. “Enumerating Trees.” Luther’s Meanderings. https://www.cs.virginia.edu/~lat7h/blog/posts/434.html.