Continuations: a warm-up

Continuations and continuation-passing style (CPS) are introduced in The Little Schemer, chapter 8, using collectors: functions that collect values, through being repeatedly redefined. It was a tough chapter for me, but the idea is simple once you get it, so I’d like to leave some help for others. I’ll use Ruby for the examples, with some JavaScript and Scheme at the end.

In languages with first-class functions, you can assign functions to variables, and re-assign those variables. Consider this Ruby example:

func = lambda { |x| puts x }

['a', 'b', 'c'].each { |ch|
    old_func = func
    func = lambda { |x| old_func[x + ch] }

func['d']  #=> prints 'dcba'

By re-defining func in terms of old_func, we’re building up a recursive computation. It’s like normal recursion, but approached from the other side — without explicit definitions. Since a Ruby function is a closure, it remembers the value of the variables in scope when it was created; each layer of this recursion holds its own value for ch and old_func. When we call the last func, it sees ch = ‘c’ and x = ‘d’. It concatenates them, and calls its version of old_func…which sees x = ‘dc’ and ch = ‘b’, concatenates them, and passes it to its old_func, and so on.In fact, if we wrote it like this, the execution of all those lambdas would be exactly the same:

func_puts = lambda { |x| puts x }
func_add_a = lambda { |x| func_puts[x + 'a'] }
func_add_b = lambda { |x| func_add_a[x + 'b'] }
func_add_c = lambda { |x| func_add_b[x + 'c'] }
func_add_c['d']  #=> prints 'dcba'

We could calculate factorials this way:

def factorial(n, func)
    if n == 1
        factorial(n - 1, lambda { |i| func[i * n] })
factorial(3, lambda { |fact| puts fact })  #=> prints 6
  1. On the first call to factorial, n = 3, and func just prints its argument. But func isn’t called yet…since n isn’t 1, we recurse with n - 1 = 2, and a new function that calls func with its argument i times 3.
  2. On the recurse, n = 2, and func calls the original printer func with its argument i times 3. Since n still isn’t 1, we recurse again, with n - 1 = 1, and a new function that calls our func with its argument i times 2.
  3. On the final round, n = 1, so we (finally!) call func with 1, which…
  4. …calls the previous func with 1 * 2, which…
  5. …calls the original func with (1 * 2) * 3, which…
  6. prints 6.

As factorial recurses, it builds up a recursive tower of func calls. In factorial‘s base case, the last (and outermost) func is called, and we begin to climb down the func tower, to its bottom floor, the original func. It’s recursion in two directions.

In case Ruby isn’t your favorite language, here are versions in JavaScript and Scheme:

function factorial(n, func) {
    if (n == 1)
        factorial(n - 1, function(i) {
            func(n * i)

factorial(3, function(fact) { print(fact) })
; Too bad WordPress doesn't format Scheme or Lisp!
(define factorial
  (lambda (n func)
    (cond ((= n 1) (func 1))
          (else (factorial (- n 1)
                           (lambda (i) (func (* n i))))))))

; Here, the original func is just an identity function.
(factorial 4 (lambda (fact) fact))

Once this is clear, you can see many other applications:

  • You could find all the even numbers in a list: instead of passing a number to func, pass the list of even numbers, and add each even number in.
  • You could separate the numbers into evens and odds: instead of passing just one list to func, pass two lists, for evens and odds, and add each number to the correct list.
  • You could separate any list of items by any criteria: instead of hard-coding “is even?” into the function, pass a predicate function. (Maybe you want to extract all occurrences of ‘tuna’ from a list of words.)

That should be enough of a warm-up for chapter 8. See you in chapter 9, when they derive the Y-combinator!