Let’s Program a Calculus Student

5 April 2022

Last week I did a little Haskell show-off for two friends. Besides the classical infinite list of primes one-liner and mandatory factorial and Fibonacci functions, I also wanted something more complex. Specifically, since they work with Graphical Linear Algebra, I wanted to show them how nice it is to write DSLs in Haskell. It feels almost too natural. You write your types as if they are grammars, your functions as if they are rewriting rules and bang, by the magic of recursion everything works.

I offered them what I consider the perfect exhibition for this: Let’s make a solver for a Calculus exam!

Calculus is a subject that in their College years, everybody learns to respect (or fear). Thus, at first sight this may seem too monumental of a task for a mere exposition. But what if I told you that if we restrict ourselves to derivatives, it takes about a hundred lines of code? A lot of people are not used to thinking of Calculus this way, but computing derivatives is actually a pretty straightforward algorithm.

One thing that one of those friends, who is a Professor in the Department of Computer Science, said really resonated with me: “People would struggle much less with math if they learned in school how to write syntax trees.”1

I really liked this phrase and would add even more: learning about syntax trees (and their siblings s-expressions) and recursion eased my way not only with math but with learning grammar as well. How I wish that math and languages classes from school worked with concepts that are as uniform as they could. Well, enough rambling. Time to do some programming!

Please be rational

Before delving into the depths of first-year undergraduate math, let’s take a step back and start with something simpler: rational functions.

module Calculus.Fraction where

A rational function is formed of sums, products, and divisions of numbers and a indeterminate symbol, traditionally denoted by x. An example is something like

\frac{32x^4 + \frac{5}{4}x^3 - x + 21}{\frac{5x^{87} - 1}{23x} + 41 x^{76}}.

Let’s construct the rational functions over some field of numbers a. It should have x, numbers (called constants), and arithmetic operations between them.

data Fraction a = X
                | Const a
                | (Fraction a) :+: (Fraction a)
                | (Fraction a) :*: (Fraction a)
                | (Fraction a) :/: (Fraction a)
  deriving (Show, Eq)

I choose to give it the name Fraction because rational functions are represented by fractions of polynomials. We make it a parameterized type because a could be any numeric field, just like in math we use the notations \mathbb{Q}(x), \mathbb{C}(x), \mathbb{Z_{17}}(x) to denote the rational functions over different fields.

Since we are using operator constructors, let’s give them the same associativity and fixity as the built-in operators.

infixl 6 :+:      -- Left associative
infixl 7 :*:, :/: -- Left associative with higher precedence than :+:

For now our constructors are only formal, they just create syntax trees:

ghci> Const 2 :+: Const 2 :+: X
(Const 2 :+: Const 2) :+: X
it :: Num a => Fraction a

We can teach it how to simplify these equations but since the focus here is on derivatives, we will postpone this to a further section. Let’s say that right now our student will just solve the problems and return the exam answers in long-form without simplifying anything.

The next thing is thus teach it how to evaluate an expression at a value. The nice part is that in terms of implementation, that’s equivalent to writing an interpreter from the Fractions to the base field.

eval :: Fractional a => Fraction a -> a -> a
eval X         c = c
eval (Const a) _ = a
eval (f :+: g) c = eval f c + eval g c
eval (f :*: g) c = eval f c * eval g c
eval (f :/: g) c = eval f c / eval g c

This is it. Our evaluator traverses the expression tree by turning each X leaf into the value c, keeping constants as themselves, and collapsing the nodes according to the operation they represent. As an example:

ghci> p = X :*: X :+: (Const 2 :*: X) :+: Const 1
p :: Num a => Fraction a
ghci> eval p 2
it :: Fractional a => a

What is a number after all?

One nicety about languages like Haskell is that they are not only good for writing DSls, but they are also good for writing embedded DSLs. That is, something like our symbolic Fractions can look like just another ordinary part of the language.

It won’t be nice to just write X^2 + 2*X + 1 instead of the expression we evaluated above?

Well, we first need to teach or program how to use the built-in numeric constants and arithmetic operations. We achieve this through the typeclasses Num and Fractional. This is kind of Haskell’s way of saying our type forms a Ring and Field.

instance Num a => Num (Fraction a) where
 -- For the operations we just use the constructors
 (+) = (:+:)
 (*) = (:*:)
 -- This serves to embed integer constants in our Ring.
 -- Good for us that we already have a constructor for that.
 fromInteger n = Const (fromInteger n)
 -- This one is how to do `p -> -p`.
 -- We didn't define subtraction, so let's just multiply by -1.
 negate p      = Const (-1) :*: p
 -- These ones below are kinda the problem of `Num`...
 -- As much as I don't like runtime errors,
 -- for this exposition I think the best is
 -- to just throw an error if the user tries to use them.
 abs    = error "Absolute value of a Fraction is undefined"
 signum = error "Sign of a Fraction is undefined"

This makes our type into a Ring and we can now use constants, + and * with it. The code to make it into a Field is equally straightforward.

instance Fractional a => Fractional (Fraction a) where
 (/) = (:/:)
 fromRational r = Const (fromRational r)

Let’s see how it goes

ghci> (X^2 + 2*X + 1) / (X^3 - 0.6)
(((X :*: X) :+: (Const 2.0 :*: X)) :+: Const 1.0) :/: (((X :*: X) :*: X) :+: (Const (-1.0) :*: Const 0.5))
it :: Fractional a => Fraction a

What we wrote is definitely much cleaner than the internal representation. But there is still one more nicety: Doing this also gave us the ability to compose expressions! Recall the type of our evaluator function:

eval :: Fractional field => Fraction field -> field -> field

But we just implemented a Fractional (Fraction a) instance! Thus, as long as we keep our Fractions polymorphic, we can evaluate an expression at another expression.

ghci> eval (X^2 + 3) (X + 1)
((X :+: Const 1.0) :*: (X :+: Const 1.0)) :+: Const 3.0
it :: Fractional a => Fraction a

Enough arithmetic, it’s Calculus time

Alright, alright. Time to finally teach some calculus. Remember all the lectures, all the homework… Well, in the end, what we need to differentiate a rational function are only five simple equations: 3 tree recursive rules and 2 base cases.

diff :: Fractional a => Fraction a -> Fraction a
diff X           = 1
diff (Const _)   = 0
diff (f :+: g)   = diff f + diff g
diff (f :*: g)   = diff f * g + f * diff g
diff (f :/: g)   = (diff f * g - f * diff g) / g^2

Well, that’s it. Now that we’ve tackled the rational functions, let’s meet some old friends from Calculus again.

Elementary Functions

In calculus, besides rational functions, we also have sines, cosines, exponentials, logs and anything that can be formed combining those via composition or arithmetic operations. For example:

\frac{1}{\pi}\log\left(\sin(3x^3) + \frac{e^{45x} - 21}{x^{0.49}\mathrm{asin}(-\frac{\pi}{x})}\right) + \cos(x^2)

This sort of object is called an Elementary function in the math literature but here we will call it simply an expression.

module Calculus.Expression where

Let’s create a type for our expressions then. It is pretty similar to the Fraction type from before with the addition that we can also apply some transcendental functions.

data Expr a = X
            | Const a
            | (Expr a) :+: (Expr a)
            | (Expr a) :*: (Expr a)
            | (Expr a) :/: (Expr a)
            | Apply Func (Expr a)
   deriving (Show, Eq)

data Func = Cos | Sin | Log | Exp | Asin | Acos | Atan
  deriving Show

The Func type is a simple enumeration of the most common functions that one may find running wild on a Calculus textbook. There are also other possibilities but they are generally composed from those basic building blocks.

Since the new constructor plays no role in arithmetic, We can define instances Num (Expr a) and Fractional (Expr a) that are identical to those we made before. But having these new functions also allows us to add a Floating instance to Expr a, which is sort of Haskell’s way of expressing things that act like real/complex numbers.

instance Floating a => Floating (Expr a) where
 -- Who doesn't like pi, right?
 pi      = Const pi
 -- Those are easy, we just need to use our constructors
 exp     = Apply Exp
 log     = Apply Log
 sin     = Apply Sin
 cos     = Apply Cos
 asin    = Apply Asin
 acos    = Apply Acos
 atan    = Apply Atan
 -- We can write hyperbolic functions through exponentials
 sinh x  = (exp x - exp (-x)) / 2
 cosh x  = (exp x + exp (-x)) / 2
 asinh x = log (x + sqrt (x^2 - 1))
 acosh x = log (x + sqrt (x^2 + 1))
 atanh x = (log (1 + x) - log (1 - x)) / 2

Time to update our methods

We already have our type and its instances. Now it is time to also consider derivatives of the transcendental part of the expressions. The evaluator is almost equal except for a new pattern:

eval :: Floating a => Expr a -> a -> a
eval X         c = c
eval (Const a) _ = a
eval (f :+: g) c = eval f c + eval g c
eval (f :*: g) c = eval f c * eval g c
eval (f :/: g) c = eval f c / eval g c
eval (Apply f e) c = let g = calculator f
                     in g (eval e c)

We also had to define a calculator helper that translates between our Func type and the actual functions. This is essentially the inverse of the floating instance we defined above but I couldn’t think of a way to do that with less boilerplate without using some kind of metaprogramming.2

calculator :: Floating a => Func -> (a -> a)
calculator Cos  = cos
calculator Sin  = sin
calculator Log  = log
calculator Exp  = exp
calculator Asin = asin
calculator Acos = acos
calculator Atan = atan

The derivative is pretty similar, with the difference that we implement the chain rule instead of for the Apply constructor.

Let’s start by writing a cheatsheet of derivatives. This is the kind of thing you’re probably not allowed to carry to a Calculus exam, but let’s say that our program has it stored in its head (provided this makes any sense). Our cheatsheet will get a Func and turn it into the expression of its derivative.

cheatsheet :: Floating a => Func -> Expr a
cheatsheet Sin  = cos X
cheatsheet Cos  = negate (sin X)
cheatsheet Exp  = exp X
cheatsheet Log  = 1 / X
cheatsheet Asin = 1 / sqrt (1 - X^2)
cheatsheet Acos = -1 / sqrt (1 - X^2)
cheatsheet Atan = 1 / (1 + X^2)

Finally, the differentiator is exactly the same as before except for a new pattern that looks for the derivative on the cheatsheet and evaluates the chain rule using it.

diff :: Floating a => Expr a -> Expr a
diff X           = 1
diff (Const _)   = 0
diff (f :+: g)   = diff f + diff g
diff (f :*: g)   = diff f * g + f * diff g
diff (f :/: g)   = (diff f * g - f * diff g) / g^2
diff (Apply f e) = let f' = cheatsheet f
                   in eval f' e * diff e

This way we finish our Calculus student program. It can write any elementary function as normal Haskell code, evaluate them, and symbolically differentiate them. So what do you think?

What’s next?

Although we finished our differentiator, there are a couple of topics that I think are worth discussing because they are simple enough to achieve and will make our program a lot more polished or fun to play with.


Definitely the least elegant part of our program is the expression simplifier. It is as straightforward as the rest, consisting of recursively applying rewriting rules to an expression, but there are a lot of corner cases and possible rules to apply. Besides that, sometimes which equivalent expression is the simple one can be up to debate.

We first write the full simplifier. It takes an expression and apply rewriting rules to it until the process converges, i.e. the rewriting does nothing. We use a typical tail-recursive loop for this.

simplify :: (Eq a, Floating a) => Expr a -> Expr a
simplify expr = let expr' = rewrite expr
                in if expr' == expr
                    then expr
                    else simplify expr'

From this function, we can define a new version of diff that simplifies its output after computing it.

diffS = simplify . diff

The bulk of the method is formed by a bunch of identities. You can think of them as the many math rules that a student should remember in order to simplify an expression while solving a problem. Since there is really no right answer3 when we are comparing equals with equals, any implementation will invariably be rather ad hoc. One thing to remember though is that the rules should eventually converge. If you use identities that may cancel each other, the simplification may never terminate.

-- Constants
rewrite (Const a :+: Const b)   = Const (a + b)
rewrite (Const a :*: Const b)   = Const (a * b)
rewrite (Const a :/: Const b)   = Const (a / b)
rewrite (Apply func (Const a))  = Const (calculator func a)
-- Associativity
rewrite (f :+: (g :+: h)) = (rewrite f :+: rewrite g) :+: rewrite h
rewrite (f :*: (g :*: h)) = (rewrite f :*: rewrite g) :*: rewrite h
-- Identity for sum
rewrite (f :+: Const 0)  = rewrite f
rewrite (Const 0 :+: f)  = rewrite f
rewrite (f :+: Const a)  = Const a :+: rewrite f
-- Identity for product
rewrite (f :*: Const 1)  = rewrite f
rewrite (Const 1 :*: f)  = rewrite f
rewrite (f :*: Const 0)  = Const 0
rewrite (Const 0 :*: f)  = Const 0
rewrite (f :*: Const a)  = Const a :*: rewrite f
-- Identity for division
rewrite (Const 0 :/: f)  = Const 0
rewrite (f :/: Const 1)  = rewrite f
-- Inverses
rewrite (f :/: h)
 | f == h = Const 1
rewrite ((f :*: g) :/: h)
 | f == h = rewrite g
 | g == h = rewrite f
rewrite (f :+: (Const (-1) :*: g))
 | f == g = Const 0
-- Function inverses
rewrite (Apply Exp  (Apply Log  f)) = rewrite f
rewrite (Apply Log  (Apply Exp  f)) = rewrite f
rewrite (Apply Sin  (Apply Asin f)) = rewrite f
rewrite (Apply Asin (Apply Sin  f)) = rewrite f
rewrite (Apply Cos  (Apply Acos f)) = rewrite f
rewrite (Apply Acos (Apply Cos  f)) = rewrite f
rewrite (Apply Atan ((Apply Sin  f) :/: (Apply Cos g)))
  | f == g = rewrite f
-- Recurse on constructors
rewrite (f :+: g)   = rewrite f :+: rewrite g
rewrite (f :*: g)   = rewrite f :*: rewrite g
rewrite (f :/: g)   = rewrite f :/: rewrite g
rewrite (Apply f e) = Apply f (rewrite e)
-- Otherwise stop recursion and just return itself
rewrite f = f

I find it interesting to look at the size of this rewrite function and think of the representation choices we made along this post. There are many equivalent ways to write the same thing, forcing us to keep track of all those equivalence relations.

Taylor series

One of the niceties of working with a lazy language is how easy it is to work with infinite data structures. In our context, we can take advantage of that to write the Taylor Series of an expression.

The Taylor series of f at a point c is defined as the infinite sum

f(x) = \sum_{n = 0}^\infty \frac{f^{(n)}(c)}{n!} (x-c)^n.

Let’s first write a function that turns an expression and a point into an infinite list of monomials. We do that by generating a list of derivatives and factorials, which we assemble for each natural number.

taylor :: (Eq a, Floating a) => Expr a -> a -> [Expr a]
taylor f c = simplify <$> zipWith3 assemble [0..] derivatives factorials
  assemble n f' nfat = Const (eval f' c / nfat) * (X - Const c)^n
  -- Infinite list of derivatives [f, f', f'', f'''...]
  derivatives = iterate diff f
  -- Infinite list of factorials [0!, 1!, 2!, 3!, 4!...]
  factorials  = fmap fromInteger factorials'
  factorials' = 1 : zipWith (*) factorials' [1..]

We can also write the partial sums which only have N terms of the Taylor expansion. These have the computational advantage of actually being evaluable.

approxTaylor :: (Eq a, Floating a) => Expr a -> a -> Int -> Expr a
approxTaylor f c n = (simplify . sum .take n) (taylor f c)

At last, a test to convince ourselves that it works.

ghci> g = approxTaylor (exp X) 0
g :: (Eq a, Floating a) => Int -> Expr a
ghci> g 10
((((((((Const 1.0 :+: X) :+: ((Const 0.5 :*: X) :*: X)) :+: (((Const 0.16666666666666666 :*: X) :*: X) :*: X)) :+: ((((Const 4.1666666666666664e-2 :*: X) :*: X
) :*: X) :*: X)) :+: (((((Const 8.333333333333333e-3 :*: X) :*: X) :*: X) :*: X) :*: X)) :+: ((((((Const 1.388888888888889e-3 :*: X) :*: X) :*: X) :*: X) :*: X
) :*: X)) :+: (((((((Const 1.984126984126984e-4 :*: X) :*: X) :*: X) :*: X) :*: X) :*: X) :*: X)) :+: ((((((((Const 2.48015873015873e-5 :*: X) :*: X) :*: X) :*
: X) :*: X) :*: X) :*: X) :*: X)) :+: (((((((((Const 2.7557319223985893e-6 :*: X) :*: X) :*: X) :*: X) :*: X) :*: X) :*: X) :*: X) :*: X)
it :: (Eq a, Floating a) => Expr a
ghci> eval (g 10) 1
it :: (Floating a, Eq a) => a


This post only exists thanks to the great chats I had with João Paixão and Lucas Rufino. Besides listening to me talking endlessly about symbolic expressions and recursion, they also asked a lot of good questions and provided the insights that helped shape what became this post.

I also want to thank the people on reddit that noticed typos and gave suggestions for code improvement.

  1. The phrase wasn’t exactly that. It had a better effect. But it has been almost a week and I have the memory of a goldfish. The intention is preserved thought.↩︎

  2. If you have any suggestions to make this code mode elegant, feel free to contact me and we can edit it. :)↩︎

  3. Countless times I’ve only got to finish a proof because I’ve used an identity on the non-intuitive side. For example writing something like f(x) = f(x) \cdot 1 = f(x) \cdot (\sin(x)^2 + \cos(x)^2). Those are always some great A-ha! moments.↩︎

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