Finger Trees in Agda

Posted on February 25, 2019
Tags: Agda

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As I have talked about previously, a large class of divide-and conquer algorithms rely on “good” partitioning for the divide step. If you then want to make the algorithms incremental, you keep all of those partitions (with their summaries) in some “good” arrangement (Mu, Chiang, and Lyu 2016). Several common data structures are designed around this principle: binomial heaps, for instance, store partitions of size 2n2^n. Different ways of storing partitions favours different use cases: switch from a binomial heap to a skew binomial, for instance, and you get constant-time cons.

The standout data structure in this area is Hinze and Paterson’s finger tree (Hinze and Paterson 2006). It caches summaries in a pretty amazing way, allowing for (amortised) 𝒪(1)\mathcal{O}(1) cons and snoc and 𝒪(logn)\mathcal{O}(\log n) split and append. These features allow it to be used for a huge variety of things: Data.Sequence uses it as a random-access sequence, but it can also work as a priority queue, a search tree, a priority search tree (Hinze 2001), an interval tree, an order statistic tree…

All of these applications solely rely on an underlying monoid. As a result, I thought it would be a great data structure to implement in Agda, so that you’d get all of the other data structures with minimal effort (similar thinking motivated a Coq implementation; Sozeau 2007).

Scope of the Verification

There would be no real point to implementing a finger tree in Agda if we didn’t also prove some things about it. The scope of the proofs I’ve done so far are intrinsic proofs of the summaries in the tree. In other words, the type of cons is as follows:

cons :  x {xs}  Tree xs  Tree (μ x ∙ xs)

This is enough to prove things about the derived data structures (like the correctness of sorting if it’s used as a priority queue), but it’s worth pointing out what I haven’t proved (yet):

  1. Invariants on the structure (“safe” and “unsafe” digits and so on).
  2. The time complexity or performance of any operations.

To be honest, I’m not even sure that my current implementation is correct in these regards! I’ll probably have a go at proving them in the future (possibly using Danielsson 2008).

Monoids and Proofs

The bad news is that finger trees are a relatively complex data structure, and we’re going to need a lot of proofs to write a verified version. The good news is that monoids (in contrast to rings) are extremely easy to prove automatically. In this project, I used reflection to do so, but I think it should be possible to do with instance resolution also.


First things first, we need a way to talk about the summaries of elements we’re interested in. This is captured by the following record type:

record σ {a} (Σ : Set a) : Set (a ⊔ r) where
    μ : Σ  𝓡
open σ ⦃ ...

𝓡 is the type of the summaries, and μ means “summarise”. The silly symbols are used for brevity: we’re going to be using this thing everywhere, so it’s important to keep it short. Here’s an example instance for lists:

  σ-List :  {a} {Σ : Set a} _ : σ Σ ⦄  σ (List Σ)
  μ ⦃ σ-List ⦄ = List.foldr (__ ∘ μ) ε

Working With Setoids

As I mentioned, the tree is going to be verified intrinsically. In other word its type will look something like this:

Tree : 𝓡  Set

But before running off to define that the obvious way, I should mention that I made the annoying decision to use a setoid (rather than propositional equality) based monoid. This means that we don’t get substitution, making the obvious definition untenable.

I figured out a solution to the problem, but I’m not sure if I’m happy with it. That’s actually the main motivation for writing this post: I’m curious if other people have better techniques for this kind of thing.

To clarify: “this kind of thing” is writing intrinsic (correct-by-construction) proofs when a setoid is involved. Intrinsic proofs usually lend themselves to elegance: to prove that map preserves a vector’s length, for instance, basically requires no proof at all:

map :  {a b n} {A : Set a} {B : Set b}
     (A  B)
     Vec A n
     Vec B n
map f [] = []
map f (x ∷ xs) = f x ∷ map f xs

But that’s because pattern matching works well with propositional equality: in the first clause, n is set to 0 automatically. If we were working with setoid equality, we’d instead maybe get a proof that n ≈ 0, and we’d have to figure a way to work that into the types.


The first part of the solution is to define a wrapper type which stores information about the size of the thing it contains:

record μ⟨_⟩≈_ {a} (Σ : Set a)_ : σ Σ ⦄ (𝓂 : 𝓡) : Set (a ⊔ r ⊔ m) where
  constructor _⇑[_]
    𝓢 : Σ
    𝒻 : μ 𝓢 ≈ 𝓂

Technically speaking, I think this is known as a “fibre”. μ⟨ Σ ⟩≈ 𝓂 means “There exists a Σ such that μ Σ ≈ 𝓂”. Next, we’ll need some combinators to work with:

infixl 2 _≈[_]
_≈[_] :  {a} {Σ : Set a}_ : σ Σ ⦄ {x : 𝓡}  μ⟨ Σ ⟩≈ x   {y}  x ≈ y  μ⟨ Σ ⟩≈ y
𝓢 (xs ≈[ y≈z ]) = 𝓢 xs
𝒻 (xs ≈[ y≈z ]) = trans (𝒻 xs) y≈z

This makes it possible to “rewrite” the summary, given a proof of equivalence.

Do Notation

The wrapper on its own isn’t enough to save us from hundreds of lines of proofs. Once you do computation on its contents, you still need to join it up with its original proof of equivalence. In other words, you’ll need to drill into the return type of a function, find the place you used the relevant type variable, and apply the relevant proof from the type above. This can really clutter proofs. Instead, we can use Agda’s new support for do notation to try and get a cleaner notation for everything. Here’s a big block of code:

infixl 2 arg-syntax
record Arg {a} (Σ : Set a)_ : σ Σ ⦄ (𝓂 : 𝓡) (f : 𝓡  𝓡) : Set (m ⊔ r ⊔ a) where
  constructor arg-syntax
    ⟨f⟩ : Congruent₁ f
    arg : μ⟨ Σ ⟩≈ 𝓂
open Arg

syntax arg-syntax  sz  e₁) xs = xs [ e₁ ⟿ sz ]

infixl 1 _>>=_
_>>=_ :  {a b} {Σ₁ : Set a} {Σ₂ : Set b}_ : σ Σ₁ ⦄ ⦃ _ : σ Σ₂ ⦄ {𝓂 f}
       Arg Σ₁ 𝓂 f
       ((x : Σ₁)  ⦃ x≈ : μ x ≈ 𝓂 ⦄  μ⟨ Σ₂ ⟩≈ f (μ x))
       μ⟨ Σ₂ ⟩≈ f 𝓂
arg-syntax cng xs >>= k = k (𝓢 xs) ⦃ 𝒻 xs ⦄ ≈[ cng (𝒻 xs) ]

First, we define a wrapper for types parameterised by their summary, with a way to lift an underlying equality up into some expression f. The >>= operator just connects up all of the relevant bits. An example is what’s needed:

listToTree :  {a} {Σ : Set a}_ : σ Σ ⦄  (xs : List Σ)  μ⟨ Tree Σ ⟩≈ μ xs
listToTree [] = empty ⇑
listToTree (x ∷ xs) = [ ℳ ↯ ]≈ do
  ys ← listToTree xs [ μ x ∙> s ⟿ s ]
  x ◂ ys

The first line is the base case, nothing interesting going on there. The second line begins the do-notation, but first applies [ ℳ ↯ ]≈: this calls the automated solver. The second line makes the recursive call, and with the syntax:

[ μ x ∙> s ⟿ s ]

It tells us where the size of the bound variable will end up in the outer expression.

Danielsson, Nils Anders. 2008. “Lightweight Semiformal Time Complexity Analysis for Purely Functional Data Structures.” In Proceedings of the 35th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, 133–144. POPL ’08. New York, NY, USA: ACM. doi:10.1145/1328438.1328457.

Hinze, Ralf. 2001. “A Simple Implementation Technique for Priority Search Queues.” In Proceedings of the 2001 International Conference on Functional Programming, 110–121. ACM Press. doi:10.1145/507635.507650.

Hinze, Ralf, and Ross Paterson. 2006. “Finger Trees: A Simple General-Purpose Data Structure.” Journal of Functional Programming 16 (2): 197–217.

Mu, Shin-Cheng, Yu-Hsi Chiang, and Yu-Han Lyu. 2016. “Queueing and Glueing for Optimal Partitioning (Functional Pearl).” In Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming, 158–167. ICFP 2016. New York, NY, USA: ACM. doi:10.1145/2951913.2951923.

Sozeau, Matthieu. 2007. “Program-Ing Finger Trees in Coq.” In Proceedings of the 12th ACM SIGPLAN International Conference on Functional Programming, 13–24. ICFP ’07. New York, NY, USA: ACM. doi:10.1145/1291151.1291156.