Automatic Ring Solving

Today’s sorta-review is of Automatically and Efficiently Illustrating Polynomial Equations in Agda by Donnacha Oisin Kidney. I say it’s sorta a review because I had to write some annoying proofs recently, and discovered that Agda has a ring solver that automates annoying proofs. For example, it can solve things like (a + b) * (a + b) = a^2 + 2*a*b + b^2, which is rather amazing if you think about it. I got curious about how this is possible, and came across AaEIPEiA, quickly skimmed it for the rough approach, and then decided to write my own ring solver. As a result, this post is certainly inspired by AaEIPiA, but my implementation is extremely naive compared to the one presented in the paper. Kidney’s paper is very good, and I apologize for not doing it justice here.

So, some background. Agda lets you write types that correspond to equalities, and values of those types are proofs of those equalities. For example, we can write the following type:

(x :)  (x + 1) * (x + 1)(x * x) + (1 + 1) * x + 1

You probably wouldn’t write this for its own sake, but it might come up as a lemma of something else you’re trying to prove. However, actually proving this equality is a huge amount of busywork, that takes forever, and isn’t actually interesting because we all know that this equality holds. For example, the proof might look something like this:

    (x + 1) * (x + 1)
  ≡⟨ *-+-distrib (x + 1) x 1
    (x + 1) * x + (x + 1) * 1
  ≡⟨ cong (\φ -> ((x + 1) * x + φ)) $ *-1-id-r (x + 1)
    (x + 1) * x + (x + 1)
  ≡⟨ cong (\φ -> φ + (x + 1)) $ *-comm (x + 1) x ⟩
    x * (x + 1) + (x + 1)
  ≡⟨ cong (\φ -> φ + (x + 1)) $ *-+-distrib x x 1
    (x * x + x * 1) + (x + 1)
  ≡⟨ ? ⟩
    -- kill me
  ≡⟨ ? ⟩
    (x * x) + (1 + 1) * x + 1

It’s SO MUCH WORK to do nothing! This is not an interesting proof! A ring solver lets us reduce the above proof to:

    (x + 1) * (x + 1)
  ≡⟨ solve ⟩
    (x * x) + (1 + 1) * x + 1

or, even more tersely:


So that’s the goal here. Automate stupid, boring proofs so that we as humans can focus on the interesting bits of the problem.

I Don’t Even Know What a Ring Is🔗

Why is this called a ring solver? I don’t exactly know, but a ring is some math thing. My guess is that it’s the abstract version of an algebra containing addition and multiplication, with all the usual rules.

And looking at it, sure enough! A ring is a set with two monoids on it, one corresponding to addition, and the other to multiplication. Importantly, we require that multiplication distributes over addition.

Rings technically have additive inverses, but I didn’t end up implementing (or needing them.) However, I did require commutativity of both addition and multiplication — more on this later.

The ring laws mean that algebra works in the way we expect arithmetic to work. We can shuffle things around, and probably all have enough experience solving these sorts of problems with pen and paper. But what’s the actual algorithm here?

How Do You Solve A Ring?🔗

At first blush, this sounds like a hard problem! It feels like we need to see if there’s a way to turn some arbitrary expression into some other arbitrary expression. And that is indeed true, but it’s made easier when you realize that polynomials have a normal form as a sum of products of descending powers. For example, this is in normal form:

5*x^2 - 3*x + 0

The problem thus simplifies to determining if two expressions have the same normal form. Thus, we can construct a proof that each expression is equal to its normal form, and then compose those proofs together to show the unnormalized forms are equal.

My implementation is naive, and only works for expressions with a single variable, but I think the approach generalizes if you can find a suitable normal form for multiple variables.

All of this sounds like a good tack, but the hard part is convincing ourselves (and perhaps more importantly, Agda,) that the stated relationship holds. As it happens, we require three equivalent types:

  • A, the ring we’re actually trying to solve
  • Poly, a syntactic representation of the ring operations
  • Horner, the type of A-normal forms

Poly and Horner are indexed by A, but I’ve left that out for presentation purposes. Furthermore, they’re also both indexed by the degree of the polynomial, that is, the biggest power they contain. I’m not sure this was necessary, but it helped me make sure my math was right when I was figuring out how to multiply Horners.

At a high level, solving a ring equality is really a statement about how A is related to Poly and Horner. We can construct an A-expression by substituting an A for all the variables in a Poly:

construct : {n :}  Poly n  A  A

and we can normalize any syntactic expression:

normalize : {n :}  Poly n  Horner n

thus we can solve a ring equation by hoisting a proof of equality of its normal forms into a proof of equality of its construction:

    : {n :}
     (x y : Poly n)
     normalize x ≡ normalize y
     (a : A)
     construct x a ≡ construct y a

This approach is a bit underwhelming, since we need to explicitly construct syntactic objects (in Poly) corresponding to the expressions we’re trying to solve (in A). But this is something we can solve with Agda’s macro system, by creating the Polys by inspecting the actual AST, so we’ll consider the approach good enough. Today’s post is about understanding how to do ring solving, not about how to engineer a nice user-facing interface.

The actual implementation of solve is entirely straight-forward:

solve x y eq a =
    construct x a             ≡⟨ construct-is-normal x a ⟩
    evaluate (normalize x) a  ≡⟨ cong (\φ  evaluate φ a) eq ⟩
    evaluate (normalize y) a  ≡⟨ sym $ construct-is-normal y a ⟩
    construct y a

given a lemma that construct is equal to evaluating the normal form:

    : {N :}
     (x : Poly N)
     (a : A)
     construct x a ≡ evaluate (normalize x) a

The implementation of this is pretty straightforward too, requiring only that we have + and * homomorphisms between Horner and A:

    :  {m n} j k a
     evaluate {m} j a +A evaluate {n} k a ≡ evaluate (j +H k) a

    :  {m n} j k a
     evaluate {m} j a *A evaluate {n} k a ≡ evaluate (j *H k) a

These two lemmas turn out to be the hard part.

But First, Types🔗

Before we get into all of that, let’s first discuss what each of the types looks like. We have Poly, which again, is an initial encoding of the ring algebra:

data Poly : Set where
  con : A  Poly 0
  var : Poly 1
  _:+_ : {m n :}  Poly m  Poly n  Poly (m ⊔ n)
  _:*_ : {m n :}  Poly m  Poly n  Poly (m + n)

We can reify the meaning of Poly by giving a transformation into A:

construct : {N :}  Poly N  A  A
construct (con x) a = x
construct var a = a
construct (p :+ p2) a = construct p a +A construct p2 a
construct (p :* p2) a = construct p a *A construct p2 a

Our other core type is Horner, which is an encoding of the Horner normal form of a polynomial:

data Horner : Set where
  PC : A  Horner 0
  PX : {n :}  A  Horner n  Horner (suc n)

Horner requires some discussion. Horner normal form isn’t the same normal form presented earlier, instead, it’s a chain of linear multiplications. For example, we earlier saw this:

5*x^2 - 3*x + 0

in Horner normal form, this would be written as

0 + x * (3 + x * 5)

The idea is we can write any polynomial inductively by nesting the bigger terms as sums inside of multiplications against x. We can encode the above as a Horner like this:

PX 0 (PX 3 (PC 5))

and then reify the meaning of Horner with respect to A via evaluate:

evaluate : {n :}  Horner n  A  A
evaluate (PC x) v = x
evaluate (PX x xs) v = x +A (v *A evaluate xs v)

Operations on Horners🔗

We can define addition over Horner terms, which is essentially zipWith (+A):

_+H_ : {m n :}  Horner m  Horner n  Horner (m ⊔ n)
_+H_ (PC x)    (PC y)    = PC (x +A y)
_+H_ (PC x)    (PX y ys) = PX (x +A y) ys
_+H_ (PX x xs) (PC y)    = PX (x +A y) xs
_+H_ (PX x xs) (PX y ys) = PX (x +A y) (xs +H ys)

We can also implement scalar transformations over Horner, which is exactly a monomorphic fmap:

scalMapHorner : {m :}  (A  A)  Horner m  Horner m
scalMapHorner f (PC x) = PC (f x)
scalMapHorner f (PX x xs) = PX (f x) (scalMapHorner f xs)

and finally, we can define multiplication over Horner terms:

_*H_ : {m n :}  Horner m  Horner n  Horner (m + n)
_*H_ (PC x) y = scalMapHorner (x *A_) y
_*H_ (PX {m} x xs) (PC y) = scalMapHorner (_*A y) (PX x xs)
_*H_ (PX {m} x xs) yy =
  scalMapHorner (x *A_) yy +H PX #0 (xs *H yy)

The first two cases here are straightforward, just scalMapHorner-multiply in the constant value and go on your way. The PX-PX case is rather complicated however, but corresponds to the *-+-distrib law:

*-+-distrib : ∀ x xs yy → (x + xs) * yy ≡ x * yy +A xs * yy

We take advantage of the fact that we know x is a scalar, by immediately multiplying it in via scalMapHorner.

Tying it All Together🔗

As alluded to earlier, all that’s left is to show evaluate-homomorphisms for +H/+A and *H/*A:

    :  {m n} j k a
     evaluate {m} j a +A evaluate {n} k a ≡ evaluate (j +H k) a

    :  {m n} j k a
     evaluate {m} j a *A evaluate {n} k a ≡ evaluate (j *H k) a

There’s nothing interesting in these proofs, it’s just three hundred ironic lines of tedious, boring proofs, of the sort that we are trying to automate away.

Given these, we can implement construct-is-normal

    : {N :}
     (x : Poly N)
     (a : A)
     construct x a ≡ evaluate (normalize x) a
construct-is-normal (con x) a = refl
construct-is-normal var a = refl
construct-is-normal (x :+ y) a
  rewrite construct-is-normal x a
        | construct-is-normal y a
        | +A-+H-homo (normalize x) (normalize y) a
        = refl
construct-is-normal (x :* y) a
  rewrite construct-is-normal x a
        | construct-is-normal y a
        | *A-*H-homo (normalize x) (normalize y) a
        = refl


The homomorphism proofs are left as an exercise to the reader, or you can go look at the code if you want to skip doing it.

Agda Woes🔗

My implementation isn’t 100% complete, I still need to prove that *H is commutative:

*H-comm :  j k  j *H k ≡ k *H j

which shouldn’t be hard, because it is commutative. Unfortunately, Agda has gone into hysterics, and won’t even typecheck the type of *H-comm, because it can’t figure out that m + n = n + m (the implicit indices on the result of *H). As far as I can tell, there is no easy fix here; there’s some weird cong-like thing for types called subst, but it seems to infect a program and push these weird-ass constraints everywhere.

This is extremely frustrating, because it’s literally the last thing to prove after 300 grueling lines of proof. And it’s also true and isn’t even hard to show. It’s just that I can’t get Agda to accept the type of the proof because it’s an idiot that doesn’t know about additive commutativity. After a few hours of fighting with getting this thing to typecheck, I just said fuck it and postulated *H-comm.

Stupid Agda.

If you know what I’ve done wrong to deserve this sort of hell, please let me know. It would be nice to be able to avoid problems like this in the future, or resolve them with great ease.


So, that’s it! Modulo a postulate, we’ve managed to implement a ring-solver by showing the equivalence of three different representations of the same data. Just to convince ourselves that it works:

test-a : Poly 2
test-a = (var :+ con #1) :* (var :+ con #1)

test-b : Poly 2
test-b = var :* var :+ two :* var :+ con #1
    two = con #1 :+ con #1

    : (x : A)
     (x +A #1) *A (x +A #1)(x *A x) +A (#1 +A #1) *A x +A #1
success x = solve test-a test-b refl x

which Agda happily accepts!

I don’t exactly know offhand how to generalize this to multivariate polynomials, but I think the trick is to just find a normal form for them.

As usual, the code for this post is available on Github.