The mysterious Y combinator

One of the fundamental limitations of the Lambda Calculus is that the functions do not have names - they are anonymous. As a direct consequence, there is no trivial way of defining recursive functions - functions that call themselves - as functions do not have any names to be called with! However, recursion is a very powerful idea - it is used in algorithms (e.g. Divide and Conquer, Dynamic Programming, Backtracking all make use of recursion), definitions of language grammars (and thus, to some extent, their implementations), creating data structures (fun exercise: try to define a binary tree recursively). Therefore, sooner or later, the need for recursion arises.

In order to implement recursion in the Lambda Calculus, we first need to understand the concept of a fixed point. Here’s a fun exercise you can try - take a scientific calculator which is at the very least capable of calculating the elementary trigonometric functions. Enter cos(0). As expected, the calculator outputs 1. Now, enter cos(1). It should output something around 0.5403. Now enter cos(0.5403) (tip: There is a Ans button on most scientific calculators. Use that instead of typing out the answer explicitly each time). If you keep repeating this process for around 20 times, you’ll notice that all subsequent results are around 0.7389. On my calculator, after around 58 iterations, this value stops changing at 0.7390851332. On further application, cos(0.7390851332) keeps giving 0.7390851332. This is called a “Fixed point” of the cos function. Generalizing, fixed points are those values for which the result of a function application is the same value, i.e. if f is any function and x is f’s fixed point, then f(x) = x (Fun exercise: try finding the fixed point of the function x^2 + 2x + 1).

Okay, but what does it have to do with recursion?

Before implementing recursion using fixed points, I’d like to talk about the Y Combinator. The Y Combinator is simply a function that takes as an argument another function and returns its fixed points. For example, if we were to pass the Y Combinator the cosine function, we would get 0.7390851332. If we were to pass it x^2, we would get 0 (note that it could have also returned 1, which is also a valid fixed point. The actual value returned depends on the way the Y combinator is implemented).

Now, say we wanted to implement the recursive version of the factorial function as follows

fact(n) = 1, n == 0
n * fact(n - 1), n > 0


Named functions? Didn’t you say that was exactly what we were trying to avoid?

Yes and no. What we are trying to do is implement a recursive function without explicitly calling it. Observe that this is equivalent to using only those named functions that do not call themselves! Thus, we can use named functions as long as they do not call themselves.

Using the Y combinator

We first construct another function g, whose fixed point is fact. Thus, when we pass g to the Y combinator, we get back fact!

How to do that you ask? Define g as follows

g(f(n)) = 1, n == 0
n * f(n - 1), n > 0


This looks suspiciously similar to the definition of the fact function - except we don’t use g while defining g, thus making it viable to implement g in the Lambda Calculus! Also, observe that when fact is passed to g, we get back g - thus making fact a fixed point of g! In fact this can be extended to any recursive function (fun exercise: try defining an appropriate g for the Fibonacci function).

But how to implement the Y combinator?

So far, the post treats the Y combinator as some sort of a black box which takes a function and returns its fixed points. To understand how to actually implement it, consider the following definition of s (for self-application)

s x = g (x x)


for some function g, i.e. s takes a function x applies it onto itself and then applies g on the result. Now observe what happens if we feed s to itself

s s = g (s s)


Surprise! s is a fixed point of g. As g was an arbitrary function, we’ve just created a way to generate fixed points for any function! Polishing this further, we have

Y g =
let s x =
g (x x) in (s s)


which is translated into racket as

(define (Y g)
(let ([s (lambda (x)
(g (x x)))])
(s s)))


A small catch

If you try to run the above racket procedure, you’ll notice that it never returns. This is because of the order in whicn racket performs evaluation. Racket, like most schemes, is applicative order, which means that the arguments to a function are evaluated before the function is applied. Therefore, (s s) evaluates to (g (s s)), which evaluates to (g (g (s s))) and so on. To get around this, we make the assumption that the fixed point of g is always a procedure of a single argument. Note that we do not lose any expressivity because of currying. Thus, the above definition is now massaged into

(define (Y g)
(let ([s (lambda (x)
(lambda (n))
((g (x x)) n))])
(s s)))


If we were using a lazy language like Haskell, we could have avoided all this and defined the Y combinator as

y g = y (g x)