Lambda Calculus 3


Freebie: More on Evaluation

Goal: Implement Imp. Lang. repetition with Func. Lang. recursion.

Subgoal: Paradoxical Combinator

Subgoal: Implement common mathematical operations.

Warning: Maybe a bit of a slog.

Alternative treatment of the Paradoxical Combinator.

Read the notes (Chap4.pdf in ``lambda calculus'' content on BB).


The term redex, short for reducible expression, refers to subterms that can be reduced by one of the reduction rules.

A redex, or reducible expression, is a subexpression of a λ expression in which a λ can be applied to an argument. With more than one redex, there is more than one evaluation order. e.g. (+(* 3 4) (* 7 6)).

Normal Order Evaluation

Applicative Order Evaluation

E.g.: Normal order:
(λy. λz. z) ((λx.(x x))(λx.(x x))) => λz.z Since y does not appear in the body of the function
(λy. λz. z) (aka 'select-second'). The entire argument
((λx.(x x))(λx.(x x))) Is copied into formal parameter y and then thrown away.

E.g.: Applicative order:
(λy. λz. z) ((λx.(x x))(λx.(x x))) => (λy. λz. z) ((λx.(x x))(λx.(x x))) =>... Evaluation of innermost redex ((λx.(x x))(λx.(x x))) yields itself and evaluation does not terminate.


Full beta reductions: Any redex can be reduced at any time. This means essentially the lack of any particular reduction strategy: with regard to reducibility, "all bets are off".

Call by name: As normal order, but no reductions are performed inside abstractions. For example λx.(λx.x)x is in normal form according to this strategy, although it contains the redex (λx.x)x.

Call by value: Only the outermost redexes are reduced: a redex is reduced only when its right hand side has reduced to a value (variable or lambda abstraction).

Call by need: As in normal order, but function applications that would duplicate terms instead name the argument, which is then reduced only "when it is needed". Called in practical contexts "lazy evaluation". In implementations this "name" takes the form of a pointer, with the redex represented by a thunk (code to perform a delayed computation). Implements a form of "memoization".

Most programming languages (including Lisp, ML and imperative languages like C and Java) are described as "strict", meaning that functions applied to non-normalising arguments are non-normalising. This is done essentially using applicative order, call by value reduction (see below), but usually called "eager evaluation".

Applicative order is not a normalising strategy. The usual counterexample is as follows: define Ω = ωω where ω = λx.xx. This entire expression contains only one redex, namely the whole expression; its reduct is again Ω. Since this is the only available reduction, Ω has no normal form (under any evaluation strategy). Using applicative order, the expression (select-first(identity(Ω))) (λx.λy.x) (λx.x)Ω is reduced by first reducing Ω to normal form (since it is the rightmost redex), but since Ω has no normal form, applicative order fails to find a normal form for KIΩ.

In contrast, normal order is so called because it always finds a normalising reduction, if one exists. In the above example, (select-first(identity(Ω))) reduces under normal order to the first argument, identity, which is a normal form. A drawback is that redexes in the arguments may be copied, resulting in duplicated computation (for example, (λx.xx) ((λx.x)y) reduces to ((λx.x)y) ((λx.x)y) using this strategy; now there are two redexes, so full evaluation needs two more steps, but if the argument had been reduced first, there would now be none).



We can't quite define recursion with mechanisms so far. But why not just use a name from the left side of definition on its right side? def add x y = if iszero y then x else add (succ x) (pred y) Here we are hoping for (and in fact we'll soon make) something like: add one two => ... => add (succ one) (pred two) => ... => add (succ (succ (one)) (pred (pred two)) => ... => (succ (succ one)) == three


In Chapter 2 we required that all names in expressions be replaced by their definitions before the expression is evaluated. In our simple attempt above, replacement never terminates.

Problem: need recursion termination that depends on particular function arguments, which we don't know at definition time (``compile time''). e.g. stop calling yourself for the base case
( n = 0 in n!, say).

Must find a way to delay the repetitive use of the function until it is actually required (at ``run time'').

Recall our attempt: def add x y = if iszero y then x else add (succ x) (pred y) But we can't evaluate it... it takes off and never comes back.

Note here how we are using our shiny new syntax with ==. From now on we can pick the syntactic level that illustrates our issues, and not descend to λs and ()s unless it serves a purpose .


Function use always happens in an application. It may be delayed by abstraction at the point where the function is used. That is,
< function > < argument > is equivalent to
λ f. (f < argument >) < function >,

The original function becomes the argument in a new application.

In our addition example can introduce a new argument to remove (by abstraction) the recursion that's causing us trouble. def add1 f x y = if iszero y then x else f (succ x) (pred y)


Our current effort, which takes three arguments: def add1 f x y = if iszero y then x else f (succ x) (pred y) Need to find argument for add1 that has the same effect as add. Can't just pass add to add1; gives non-terminating replacement again. Also can't pass add1 in to itself--
def add = add1 add1 yields (λ f. λ x. λ y. if iszero y then x else f (succ x) (pred y)) add1 => λ x. λ y. if iszero y then x else add1 (succ x) (pred y) oops.... in the last line add1 only has two arguments, not the three it needs.


In definition of add1 the application
f (succ x) (pred y)
has only two arguments, and so substituting add1 for f leaves the application without the argument corresponding to bound variable f in add1's definition: we need three arguments, in short, not two.

What we really want to wind up with is
add1 add1 (succ x) (pred y),
so add1 may be passed on to later recursions.

So let us define an add2, this time passing (that is, copying) the argument f in as an argument of f itself. def add2 f x y = if iszero y then x else f f (succ x) (pred y) where as before
def add = add2 add2


Now the definition expands and evaluates as (λ f.λ x.λ y. if iszero y then x else f f (succ x) (pred y)) add2 => λ x.λ y. if iszero y then x else add2 add2 (succ x) (pred y)

Trick: use two copies of add2, one as function and one as argument, to allow the recursion to continue: when the recursion point is reached another copy of the whole function is passed down.

Recall our current attempt: def add2 f x y = if iszero y then x else f f (succ x) (pred y) where as before
def add = add2 add2


From now on we occasionally evaluate expressions partially in applicative order: that is, evaluate arguments before passing them to functions. Notate as ->, or
-> ... -> for a sequence.

This scheme is very natural for arithmetic: compute sub-expressions and combine: (* (+ 2 3) (- 4 1)).

If they terminate, applicative and normal order reductions yield the same result. BUT there are expressions whose evaluation terminates under normal order but does not under applicative order.

Conditional expressions are one of the problems, and that strict applicative order reduction of conditional expressions using recursive calls in a function body doesn't terminate.

The difficulty is easy to see: an if-then-else is implemented by a cond function, which takes three arguments and can be read as a "then, else, if" function, you recall. In applicative evaluation order, that means we'd evaluate the computation for the "then" and "else" cases, both, before even looking at the "if" condition! NOT what an if-then-else is supposed to do, right? Best case it's crazy, worst case, with recursion, it doesn't terminate. See the "Evaluation" chapter in the readings for more.

Thus until Chapter 8, be advised that both applicative order indicators -> and -> ... -> still imply normal order reduction of conditional expressions.




Skip down to Application and Self-Replication


We crafted a special-case recursive function for addition.

Want a constructor that does that job: build a recursive function from a non-recursive one, with a single abstraction at the recursion point.

Second example, multiplication: analogous to addition -- to multiply two numbers, add the first to the product of the first and the decremented second. If the second is zero, so is the product. def mult x y = if iszero y then zero else add x (mult x (pred y)) The last line is the infinite recursion it's our goal to fix. What we want to happen (note applicative order reductions): mult three two => ... => add three (mult three (pred two)) -> ... -> add three ( add three ( mult three (pred (pred two)))) -> ... -> add three (add three zero) -> ... -> add three three => ... => six


Again, can remove mult's self-reference by abstraction at the recursion point in a new function mult1: def mult1 f x y = if iszero y then zero else add x (f x (pred y)) But what is that f going to be? Not mult or mult1, we've been there already.

We want a function recursive to construct recursive functions from non-recursive versions:

def mult = recursive mult1

We know recursive must not only pass a copy of its argument to that argument, but also ensure that self-application will continue: the copying mechanism must also be passed on. This suggests a recursive of the form
def recursive f = f <'f' and copy> , and we'll leave the argument-copying unspecified for now.


def recursive f = f <'f' and copy>
on mult1: recursive mult1 == (λ f. (f <'f' and copy>) mult1) => mult1 <'mult1' and copy> == (λ f. λ x. λ y. if iszero y then zero else add x (f x (pred y))) <'mult1' and copy> => λ x. λ y. if iszero y then zero else add x (<'mult1' and copy> x (pred y))

In the body we have
<'mult1' and copy> x (pred y)
but we actually require:
mult1 <'mult1' and copy> x (pred y)
to assure <'mult1' and copy> is passed on again through mult1's bound variable f to the next level of recursion.


That in turn means the copy mechanism must be such that:
<'mult1' and copy> => ... => mult1 <'mult1' and copy>

In general, from function f passed to recursive, need:
<'f' and copy> => ... => f <'f' and copy>.
Thus the copy mechanism must be an application, and that application must be self-replicating.

But hey! Recall that the self-application function
λ s. (s s)
self-replicates when applied to itself!

But darn! Recall that then the self-replication never ends.

But yay! Self-application may be delayed through abstraction, namely with the construction of a new function:
λ f. λ s. (f (s s)).

In other words, that delayed self-application only happens when the new function is applied!. Just what we want for "recurse when you're called and shut up meanwhile".


Here's our abstracted self-replication function:
λ f. λ s. (f (s s)).

Here, the self-application (s s) becomes an argument for f. And this f might be a function with a conditional in its body, which only leads to the evaluation of its argument when some condition is met. Applying to arbitrary function: λ f. λ s. (f (s s)) < function > => λ s. (< function > (s s)) And applying to itself: λ s. ( (s s)) λ s. ( (s s)) => (λ s. ( (s s)) λ s. ( (s s))) Sure enough, it self-replicates just once.

"Well, we knocked the bastard off." (E.H. 1953)


def recursive
f = λ s. (f (s s)) λ s. (f (s s))

def mult = recursive mult1

CB to self: this should be pointed up, deleted, made plain... (λ f. (λ s. (f (s s)) λ s. (f (s s))) mult1) => λ s. (mult1 (s s)) λ s. (mult1 (s s)) => mult1 (λ s. (mult1 (s s)) λ s. mult1 (s s))) == (λ f. λ x. λ y. if iszero y then zero else add x (f x (pred y))) (λ s. (mult1 (s s)) λ s. (mult1 (s s))) => λ x. λ y. if iszero y then zero else add x (( λ s. (mult1 (s s)) λ s. (mult1 (s s))) x (pred y)) Don't need to replace other mult1's since its def. doesn't ref. itself.


Consider factorial: we'd LIKE to write: fac = λ n. if iszero n then 1 else n * fac(n-1) Notice in the last line fac is a reference to the name on the left: it's treated as a GLOBAL name! Those don't exist in λ calculus, though :-{. Further, as we've seen this attempt can't be evaluated since it expands the "same name" forever.

To avoid the global name and avoid infinite expansion of the same name, send in the function as an argument. fac = Y λ fac. λ n. % notice the Y! if iszero n then 1 else n * fac(n-1) In this last line, fac is a variable, not a global name (we could call it myfact or f.)


Y(f) = f ( Y( f))
And recall our last definition of factorial, with f used for the fac in the last line. That is: fac(3) = (Y λ f. λ n. if (n=0) then 1 else n*f(n-1)) (3) == (λ n. if (n=0) then 1 else n* (( Y λ f. λ n. if (n=0) then 1 else n*f(n-1)) (n-1))) (3) => if (3 = 0) then 1 else 3* ((Y λ f. λ n. if (n=0) then 1 else n*(f(n-1)))(3-1) == % (since 3 ≠ 0) 3* ((Y λ f. λ n. if (n=0) then 1 else n*(f(n-1)))(2) Which is obviously 3 * fac(2).


A rather rabbit-from-hat approach to Y. This is not a self-standing treatment: see the link below for that. This is a preview or abstract.

OUR GOAL: To create a function rec that itself creates a "recursive ready function" from its (function) argument, as in:
rec < name > = < expression > .

Recall for recursion we want to write something like def add x y = if iszero y then x else add (succ x) (pred y)

This definition itself does not terminate! Expands through space...

Abstraction (wrapping in a function) prevents a function's (like add)'s evaluation:

(f [args]) % f gets evaluated
λ g. (g [args]) % stable. Call with f
We'll try to fix our problem with add by using this 'abstraction trick'.


The abstraction trick creates a need for a self-replication function. Recall that in our add1 example, where we want to define add as (add1 add1), our problem is that single f in
def add1 f x y = if iszero y then x else f (succ x) (pred y)

which means f won't work if it expects three arguments. We know that the following version works, but we'd need to customize (note the two f's) each function we want to make recursive. def add2 f x y = if iszero y then x else f f (succ x) (pred y)

with def add = add2 add2

So to save add1 we need some way to achieve the effect of replicating f without getting into trouble. It would be ideal if f would replicate itself just once and then only when it is invoked, thus creating its own first argument.

We think of the self-application function selfapply:
def selfapply = λ s. (s s)
However, self-apply is not self-replicating:
(selfapply selfapply)
runs forever, not expanding in space but calling and copying the application itself forever through time.

Abstraction to the rescue again!

def recursive f =
(λ s. (f (s s)) λ s. (f (s s)))
keeps the self-application from running unless called, and when it is it runs just once.

So e.g. we'd write:
def add = recursive add1

recursive AT WORK

Call recursive "Y" for short.

def Y = λ f. (λ s. (f (s s)) λ s. (f (s s))) (Y < fun >) == (λ f. (λ s. (f (s s)) λ s. (f (s s))) < fun >) => (λ s. ( < fun > ( s s )) λ s. (< fun > (s s))) => < fun > λ s. (< fun > (s s)) λ s. (< fun > (s s)) = < fun > (Y < fun >)


Here is a Y tutorial that may be useful: it justifies Y in this tops-down style, which notices the cooperation between abstraction (making a function from an expression to stop it's being evaluated) and self-replication, which we've seen we need for recursion.


Our recursive is also known as a paradoxical combinator, a fixed-point finder, or Y for short. It was found by Haskall Curry.

A fixed point of a function stays the same when the function is applied to it:
if f = xn, n > 0 , then x = 0 and x = 1 are fixed points.

If we talk of applying functions to functions, then we want
f(p) =p , where p =g(f) , or equivalently,
g(f) = f(g(f)) .

As it happens, there are infinitely many such "fixed point operators" and I think hunting them is a reasonable activity for recursive function theorists (but I don't know).

Thus our Y (and often any fixed point combinator is called Y) is mathematically defined by an elegant and easy-to-remember formula that possibly has been obscured by our work so far:

Y(f) = f (Y(f))

This is recursive, only in math. Also, you can see why applying Y several times leads to nontrivial recursion:

..... f(f(f...f(Y(f))))

Compare: recursive f = λ s. (f (s s )) λ s. (f (s s )) => f (λ s. (f (s s )) λ s. (f (s s )) == f (recursive f)

Below, we see recursive mult1, or Y(mult1), in line 11. It generates the mult1 Y(mult1) in line 13, which is what we pay Y to do. You can follow these patterns through the evaluation:

Y and the Recursion Theorem

The recursion theorem is a basic result by Kleene (of the Kleene *). You may run into it in CS Theory courses. Check out Wikipedia , where we find the relation between the recursion (and fixed-point) theorems to Y:

Here are some lecture notes on the recursion theorem,
courtesy of J. Seiferas


We can now simplify notation and sugar away the auxiliary function. Use new form:

rec < name > = < expression >

This rec name indicates that the occurrence of the name in the definition should be replaced using abstraction, and then that the paradoxical combinator should be applied to the whole of the defining expression. E.g. rec add x y = if iszero y then x else add (succ x) (pred y) for def add1 f x y = if iszero y then x else f(succ x) (pred y) and def add = recursive add1


Raising to a Power: to exponentiate (compute xy) we multiply x by ``x to the power of the decremented y'', and if y is zero the power is one. rec power x y = if iszero y then one else mult x (power x (pred y))


E.g. power two three => .... => mult two (power two (pred three)) -> ... -> mult two (mult two (power two (pred (pred three)))) -> ... -> mult two (mult two (mult two (power two (pred (pred (pred three)))))) -> ... -> mult two (mult two (mult two one)) -> ... -> mult two (mult two two) -> ... -> mult two four => ... => eight


The difference of two numbers is the difference after decrementing both, with difference of a number and zero being the number. So... rec sub x y = if iszero y then x else sub (pred x) (pred y) E.g. sub four two => .... => sub (pred four) (pred two) => .... => sub (pred (pred four)) pred( (pred two)) => .... => (pred (pred four)) => .... => two An "undocumented feature" (i.e. bug): if y > x it returns zero.


Problem: Divide by far can't deal with undefined values. Hack: let division by zero be zero and remember to check for a zero divisor.

For non-zero divisor, count how often it can be subtracted from the dividend until the dividend is smaller than the divisor. rec div1 x y = if greater y x then zero else succ (div1 (sub x y) y) def div x y = if iszero y then zero else div1 x y


Start with Absolute Difference: def abs-diff x y = add (sub x y) (sub y x) def equal x y = iszero(abs-diff x y) ... def greater x y = not(iszero (sub x y)) def greater-or-equal x y = iszero (sub y x)