These notes give an overview of fixed points, a core programming language features used to explain recursive definition.
One way we can attempt to understand what a recursive definition means is to consider a series of approximations to the definition. For example, consider the standard definition of the factorial function, as expressed in our λ-calculus:
fact = λ n. if n = 0 then 1 else n * fact (n - 1)
We want to construct a series of approximations to the
fact function. Each
approximation will be able to do one more recursive call than the approximation before it. We
can start with the version that makes no recursive calls:
fact0 = λ n. if n = 0 then 1 else error
This approximates the
fact function when it makes no recursive calls; I have
error to indicate cases that we have not yet
approximated. We could replace
error with any integer
value; the point is simply to indicate that, whatever value we choose, it is not a correct
approximation of the result of
The next version of
fact that we can approximate is the version that makes one (or
fewer) recursive calls. This version will rely on the previous approxmation to handle the
fact1 = λ n. if n = 0 then 1 else n * fact0 (n - 1)
Observe two things about this code. First:
fact1 handles both the cases
with 0 and 1 recursive calls. Second: none of our definitions so far use recursion. Other than
how to implement
error (really, anything will do), there are no mysteries about how
any of these functions work.
We can continue in the same vein, building deeper and deeper approximations of
factn+1 = λ n. if n = 0 then 1 else n * factn (n - 1)
Now, we can observe that, for any program we can write, there is a value of n large
factn is indistinguishable from the recursive definition
fact. For example, if the largest argument we ever pass to
fact256 is a good enough approximation. Of course, so
fact1024, and so forth.
However, this is a little unsatisfactory—we would like our formal understanding
fact to match our intuitive understanding, and our intuitive understanding works
for all inputs, not just inputs up to some pre-defined threshold. To get a more satisfying
understanding, we have to introduce a little bit of mathematics.
The key idea we need to steal from mathematics is that of a fixed point. The short version is that, for some function $f(x)$, $c$ is a fixed point of $f$ iff $f(c) = c$. Not every function has a fixed point; for example, the there is no integer fixed point for the function $f(x) = x + 1$. On the other hand, many functions do have fixed points; for example, $0$ is the fixed point of the sine function.
How can this help us? Well, we can start by describing our series of appoximations
fact themselves as a function; that is, we want a function
factStep factn gives
factn+1. This is
not too hard to do.
factStep = λ f. λ n. if n = 0 then 1 else n * f (n - 1)
You should be able to satisfy yourself that
factStep (factStep fact0)
fact2, and so forth. The question we then have to ask is:
factStep have a fixed point? That is, is there a
factω such that
factω? Thanks to the
theorems, one of the more significant results in the mathematics of computation, we know
that, not only does this function have a fixed point, but it is exactly the one obtained by
iterating the function. That is to say,
factω is exactly the
factStep (factStep (factStep ... (factStep fact0))).
Now, suppose that
fact is defined by iterating
fact 3 behave? Well, because
fact is the fixed point
factStep, we know that
face is equal to
fact 3 should be equal to
factStep fact 3. We can substitute into
the definition of
if isz 3 then 1 else 3 * fact (3 -
1), and so forth. Evaluating, we get to
3 * fact 2, and we have gotten to
the next recursive call, as we expect.
Hopefully, you found this brief journey into the mathematics of fixed points interesting, even
if perhaps not entirely understandable. What is important, however, is that it has given us a
mechanical way of explaining recursion. That is, rather than considering recursive definitions,
such as the one given for
fact earlier, we can define recursion using fixed points
of step functions, such as
factStep. That is to say, we want to introduce an
explicit fixed point construct
fix to our λ-calculus. Then, we can
let factStep = λ f. λ n. if n = 0 then 1 else n * f (n - 1) in let fact = fix factStep in (fact 3, fact 5)
And, the same construct can be used to define arbitrary other recursive functions. For example, the Fibonacci numbers
let fibStep = λ f. λ n. if isz n then 0 else if isz (n - 1) then 1 else f (n - 1) + f (n - 2) in let fib = fix fibStep in (fib 3, fib 5)
In fact, you could even define the type checking and evaluation functions we write in class using fixed points (albeit, in a slightly more complicated λ-calculus that we have built so far). Evaluation might begin as follows:
let evalStep = λ eval. λ t. case t of Const x -> VInt x | Plus t1 t2 -> let VInt v1 = eval e1 in let VInt v2 = eval e2 in VInt (v1 + v2) ... in let eval = fix evalStep in ...
evalStep is more complicated than
but the basic idea is the same:
evalStep computes the next iteration of the
evaluation function from the previous one. So, the fixed point of
evalStep will be
the recursive evaluation function. (You may recognize
evalStep as being similar to
the generic evaluation function we built in class recently. This should
not be surprising; there, as here, our concern was the structure of recursion.)
To finish our discussion of fixed points, we want to introduce typing and evaluation rules for
fix term. We can make a first attempt based on translating the intuitive idea
of a fixed point; that is, that if expression
t is a stepping function (of
T -> T), then
fix t should be the result of iterating that
function (of type
If you check the definitions earlier in this file, you should see that they are accepted by this typing rule. The evaluation rule seems to capture the intuition of fixed points. And, if our λ-calculus were a call-by-name calculus, like Haskell, we would be done. (In fact, you can observe that this is all you need in Haskell; look at the definitions in Fix.hs).
However, our λ-calculus is call-by-value instead. And that means to compute the
result of an application (like
t (fix t)), we need to evaluate both the function
t) and the argument (
fix t). However, this seems to leave us
back where we started: to determine the value of
fix t, we need to already have the
We can find our way out of this nest by making another observation. All of the values for which
we have wanted fixed points are themselves functions. How does this help? It means that we can
assume that the result of
fix t will be applied to at least one more argument, and
so we can delay the evaluation of the
fix t until we see that argument.
Concretely, instead of the equation
we will have the equation
Hopefully you can see how this fixes the problem: because functions are themselves values, the
fix t stops immediately. On the other hand, when applied to its
next argument, it will evaluate as before.
We can update our typing and evaluation rules to reflect the restriction of fixed points to functions, as follows.
And now we have the final (for now) account of recursion in our λ-calculus. Because we have limited recursion to functions, it is not quite as expressive as it is in Haskell. For example, we cannot write the infinite streams of names used in homework 2. But, for most practical purposes, this is not a significant restriction.
The study of recursion is one of the more intricate parts of programming language theory. These notes have given you an introduction to one way of describing and reasoning about recursion. There is still plenty to come. For example, while we have a way to talk about recursively defined values, we have equally come to rely on recursively defined types. We will return to these topics later in the course.