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Simply Typed Lambda Calculus in Agda, Without Shortcuts

1 May 2013 (programming agda correctness language)

There are many ways to introduce dependent type systems, depending on which side of the Curry-Howard lens you look through. But if your interest is more in programming languages than proof assistants, then length-indexed vectors is your Hello, World!, and an interpreter for the simply-typed lambda calculus is your FizzBuzz. I think the usual presentation of the latter is a bit of a cheat, for reasons which I will explain below. I'll also show a "non-cheating" version.

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Basics for a modular arithmetic type in Agda

11 March 2012 (programming agda haskell)

In my prevous post, I described a zipper-like representation for modular counters. That representation was well-suited for when your only operations are incrementing and decrementing. However, it'd be really cumbersome to do more complex arithmetics with it.

Then the next week, Peter Diviánszky told me about all the cool stuff that was presented at the latest Agda Implementors' Meeting, among them, the Quotient library. I still remember the time when, as a practice, I tried to implement integers in Agda without looking at the standard library. I came up with something much like the library representation, except mine had a separate constructor for zero (so I had +1+ n and -1- n). I really hated how I had to shift by one at least one of the cases to avoid ending up with two representations of zero. If only there was a way to tell Agda that those two representations actually mean the same thing...

Quotient types promise to do just that: you define a type where the constructors may be redundant so that there may be several values that should have the same semantics, and then you divide it with an equivalence relation so that the new type's values are the equivalence classes. See this example defining integers as pairs of natural numbers such that the pair (x, y) represents x-y.

I wanted to try doing the same for a proper modular integer type, by factoring integers with the equivalence relation x ∼ y ⇔ n ∣ ∣x-y∣. The point is, you take the integers, define this relation, then prove that it is indeed an equivalence (i.e. it is reflexive, symmetric and transitive), in other words, you create a setoid; then you use the Quotient type constructor to create your set-of-equivalence-classes type. After that's done, you can define functions over this quotient type by defining them over representations, and proving well-definedness, i.e. that the function maps equivalent representations to the same result.

This last step can be needlessly cumbersome when defining either non-unary functions or endomorphisms, so first of all I created a small library that makes it easier to define unary and binary operators over a quotient type. For example, to define a binary operator, all you need is a binary operator on the representation set, and a proof that the operator preserves the equivalence, thereby being agnostic to the choice of representant on both arguments:

    lift₂ : (f : Op₂ A₀) → f Preserves₂ _≈_ ⟶ _≈_ ⟶ _≈_ → Op₂ (Quotient A)

So after writing loads and loads and loads of arithmetic proofs on divisibility of absolute values, like n ∣ ∣x∣ ∧ n ∣ ∣y∣ ⇒ n ∣ ∣x + y∣, I was finally ready to define modular addition:

Mod₀ : ℕ → Setoid _ _
Mod₀ n = {!!}

Mod : ℕ → Set
Mod n = Quotient (Mod₀ n)

plus : ∀ {n} → Mod n → Mod n → Mod n
plus {n} = lift₂ _+_ proof
  where
  proof : ∀ {x y t u} → n ∣ ∣x - y∣ → n ∣ ∣t - u∣ → n ∣ ∣(x + t) - (y + u)∣
  proof = {!!}
      

Of course, the meat of the work was in actually defining Mod₀ and proof above. But after that, we can get back our old increment/decrement functions as very simple and straightforward definitions:

_+1 : ∀ {n} → Mod n → Mod n
_+1 = plus [ + 1 ]

_-1 : ∀ {n} → Mod n → Mod n
_-1 = plus [ - (+ 1) ]
      

And proving that _+1 and _-1 are inverses of each other comes down to the very simple arithmetic proof (on vanilla integers!) that

pred-suc : ∀ x → ℤpred (ℤsuc x) ≡ x
      

Of course, much more properties need to be proven. The end goal of this project should be to prove that Mod n is a commutative ring; a much more ambitious project would be proving that Mod p is a field if p is prime. Unfortunately, on my machine Agda takes more than two minutes just to display the goal and context in the following hole:

plus-comm : ∀ {n} → (x y : Mod n) → plus x y ≡ plus y x
plus-comm {n} x y = {!!}
      

so this is a problem I'll have to find a workaround for before going on. But at least I have my counters, so I can at least get back to my original goal and work on the register machine semantics. Expect the next post to be about that.

You can browse the full source code here, and track it on GitHub.

Mod-N counters in Agda

19 February 2012 (programming agda correctness haskell) (6 comments)

First of all, before I start on the actual blog post, let me put this in context. I rembember a couple of years ago when I developed an interest in functional programming languages, and Haskell in particular. There was a phase when I was able to use Haskell to solve problems in the small. I understood most of the basics of pure functional programming; then there were things I regarded as magic; and of course there was a lot of things I didn't even know that I didn't know about. But none of it did I grok.

I feel like I'm starting to get to the same level with Agda now. So this is going to be one of those "look at this cool thing I made" posts where the actual result is probably going to be trivial for actual experts of the field; but it's an important milestone for my own understanding of the subject.

I wanted to play around with simple but Turing-complete languages, and I started implementing an interpreter for a counter machine. More on that in a later post; this present post describes just the representation of register values. In the model that I implemented, values of registers are byte counters, meaning they have 256 different values, and two operations +1 and -1 that the inverses of each other. Incrementing/decrementing should roll over: 255 +1 = 0 and 0 -1 = 255.

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Unary arithmetics is even slower than you'd expect

19 April 2010 (haskell programming math agda correctness)

My coworker Encsé posted a challange use the 9-digit number problem to demonstrate interesting programming techniques. So I sat down and wrote my first practical program using dependant types, in Agda. I've been playing around with Agda previously, but this seemed like a good opportunity to try to write an actual, self-contained, provably correct program with it.

I'm not going to get into details now about the resulting Agda code, because I'm planning to present it in detail in a later post. In its current form, my program is formally proven to produce only good nine-digit numbers; the only property that still needs proving is that it finds all of them.

But the sad surprise came when I tried to actually run it. It was unbearably slow to just get to all possible combinations for the first three digits. I'm talking about 12 minutes just to list them from 123 to 423 (at which point I killed the process). For comparison, the following Haskell program, which is an implementation of the same naïve algorithm, finds the (unique) solution in 4 milliseconds:

import Control.Monad.List

fromDigits = foldr shift 0
    where shift d s = 10 * s + d

p `divides` q = q `mod` p == 0

encse :: [Int]
encse = map fromDigits $ encse' 0 []
    where encse' 9 ds = return ds
          encse' n ds = do d <- [1..9]
                           let ds' = d:ds
                               n' = n + 1
                           guard $ not (d `elem` ds)
                           guard $ n' `divides` fromDigits ds'
                           encse' n' ds'
      

So where's that slowdown coming from?

The first intuition would be that the code generated by Agda is slow because in parallel to the actual computation, it is also evaluating all kinds of proofs. But the proofs exist only in the world of types, so they shouldn't matter once the program is compiled.

The real answer is that calculating in unary representation is slow. Very, very slow. Even slower than you'd imagine.

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