Andrej has invited me to write about certain surprising functional

programs.

The first program, due to Ulrich Berger (1990), performs exhaustive

search over the “Cantor space” of infinite sequences of binary

digits. I have included references at the end. A weak form of

exhaustive search amounts to checking whether or not a total predicate

holds for all elements of the Cantor space. Thus, this amounts to

universal quantification over the Cantor space. Can this possibly be

done algorithmically, in finite time?

A stronger one amounts to

finding an example such that the predicate holds, if such an example

exists, and saying that there isn’t any otherwise.

I will use the language Haskell, but it is possible to

quickly translate the programs to e.g. ML or OCaml. The source code shown here is attached as seemingly-impossible.hs.

We could use the booleans to represent binary digits, or even the

integers, but I prefer to use a different type to avoid confusions:

> data Bit = Zero | One > deriving (Eq)

The `deriving` clause tells Haskell to figure out how to decide equality of bits automatically.

For the type of infinite sequences, we could use the built-in type of

lazy lists for most algorithms considered here. But, in order to

illustrate certain points, I will take the mathematical view and

regard sequences as functions defined on the natural numbers. The next

version of the definition of Haskell will have a built-in type of

natural numbers. For the moment, I implement it as the type of

integers:

> type Natural = Integer > type Cantor = Natural -> Bit

The operator `(#)` takes a bit `x` and a

sequence `a` and produces a new sequence `x # a` with

`x` as the head and `a` as the tail (very much like the

built-in operation `(:)` for lists):

> (#) :: Bit -> Cantor -> Cantor > x # a = \i -> if i == 0 then x else a(i-1)

Notice that the notation `\i -> ...` stands for $\lambda i. \dots$.

Next, we come to the heart of the matter, the functions that perform

exhaustive search over the Cantor space. The specification of the

function `find` is that, for any total `p`, one should

have that `find p` is always a total element of the Cantor

space, and, moreover, if there is `a` in the Cantor space with

`p a = True`, then `a = find p` is an example of such an

`a`.

> forsome, forevery :: (Cantor -> Bool) -> Bool > find :: (Cantor -> Bool) -> Cantor

Because I will have several implementations of `find`, I

have to choose one to be able to compile and run the program. A

canonical choice is the first one,

> find = find_i

but you are invited to experiment with the other ones.

For the following definition of `find_i` to make sense, you

have to take the above choice.

The function `find` takes a predicate on the Cantor

space, and hence it will typically have a $\lambda$-expression as

argument. In the following definition this is not necessary,

because `(\a -> p a) = p` by the $\eta$ rule. But I have

adopted it for the sake of clarity, as then we can read “`find(\a -> p
a)`” aloud as “find

`a`such that

`p(a)`“:

> forsome p = p(find(\a -> p a)) > forevery p = not(forsome(\a -> not(p a)))

Notice that the function `forevery` (universal quantification)

is obtained from the function `forsome` (existential

quantification) via the De Morgan Law. The functionals `forsome`

and `find_i` are defined by mutual recursion:

> find_i :: (Cantor -> Bool) -> Cantor > find_i p = if forsome(\a -> p(Zero # a)) > then Zero # find_i(\a -> p(Zero # a)) > else One # find_i(\a -> p(One # a))

The intuitive idea of the algorithm `find_i` is clear: if

there is an example starting with zero, then the result is taken

to start with zero, otherwise it must start with one. Then we

recursively build the tail using the same idea. What may not be

clear is whether the recursion eventually produces a digit,

because of the indirect recursive call via the call to

`forsome`. A mathematical proof proceeds by induction on

the modulus of uniform continuity of `p`, defined below.

It may be more natural to return an example only if there is

one, and otherwise tell there isn’t any:

> search :: (Cantor -> Bool) -> Maybe Cantor > search p = if forsome(\a -> p a) then Just(find(\a -> p a)) else Nothing

The `Maybe` type constructor is predefined by Haskell as

data Maybe a = Just a | Nothing

Type-theoretic remark: the type `Maybe a` corresponds to

the sum type $A+1$, where the only element of $1$ is called

`Nothing` and where `Just` is the insertion $A \to

A+1$.

Exercise: show that both `forsome` and `find` can be

defined directly from `search` assuming we had defined

`search` first.

Common wisdom tells us that function types don’t have decidable

equality. In fact, e.g. the function type `Integer ->
Integer` doesn’t have decidable equality because of the

Halting Problem, as is well known. However, common wisdom is not

always correct, and, in fact, some other function types do have

decidable equality, for example the type

`Cantor -> y`for

any type

`y`with decidable equality, without

contradicting Turing:

> equal :: Eq y => (Cantor -> y) -> (Cantor -> y) -> Bool > equal f g = forevery(\a -> f a == g a)

This seems strange, even fishy, because the Cantor space is in

some sense bigger than the integers. In a follow-up post,

I’ll explain that this has to do with the fact that the Cantor

space is topologically compact, but the integers are not.

Let’s run an example:

> coerce :: Bit -> Natural > coerce Zero = 0 > coerce One = 1 > f, g, h :: Cantor -> Integer > f a = coerce(a(7 * coerce(a 4) + 4 * (coerce(a 7)) + 4)) > g a = coerce(a(coerce(a 4) + 11 * (coerce(a 7)))) > h a = if a 7 == Zero > then if a 4 == Zero then coerce(a 4) else coerce(a 11) > else if a 4 == One then coerce(a 15) else coerce(a 8)

Now we call the `ghci` interpreter:

$ ghci seemingly-impossible.lhs ___ ___ _ / _ / // __(_) / /_// /_/ / / | | GHC Interactive, version 6.6, for Haskell 98. / /_/ __ / /___| | http://www.haskell.org/ghc/ ____// /_/____/|_| Type :? for help. Loading package base ... linking ... done. [1 of 1] Compiling Main ( seemingly-impossible.lhs, interpreted ) Ok, modules loaded: Main. *Main>

At this point we can evaluate expressions at the interpreter’s prompt.

First I ask it to print time and space usage after each evaluation:

*Main> :set +s

On my Dell 410 laptop running at 1.73GHz, I test the following

expressions:

*Main> equal f g False (0.10 secs, 3490296 bytes) *Main> equal f h True (0.87 secs, 36048844 bytes) *Main> equal g h False (0.09 secs, 3494064 bytes) *Main> equal f f True (0.91 secs, 38642544 bytes) *Main> equal g g True (0.15 secs, 6127796 bytes) *Main> equal h h True (0.83 secs, 32787372 bytes)

By changing the implementation of `find`, I’ll make this

faster and also will be able to run bigger examples. But let’s

carry on with the current implementation for the moment.

The following was Berger’s main motivation for considering the

above constructions:

> modulus :: (Cantor -> Integer) -> Natural > modulus f = least(\n -> forevery(\a -> forevery(\b -> eq n a b --> (f a == f b))))

This is sometimes called the *Fan Functional*, and goes back to Brouwer

(1920’s) and it is well known in the higher-type computability theory

community (see Normann (2006) below). It finds the

*modulus of uniform continuity*, defined as the least natural

number $n$ such that

$\forall \alpha,\beta(\alpha =_n \beta \to f(\alpha)=f(\beta),$

where

$\alpha =_n \beta \iff \forall i < n. \alpha_i = \beta_i.$

What is going on here is that computable functionals are continuous,

which amounts to saying that finite amounts of the output depend only

on finite amounts of the input. But the Cantor space is compact, and

in analysis and topology there is a theorem that says that continuous

functions defined on a compact space are *uniformly*

continuous. In this context, this amounts to the existence of a single

$n$ such that for all inputs it is enough to look at depth $n$ to get

the answer (which in this case is always finite, because it is an

integer). I’ll explain all this in another post. Here I will

illustrate this by running the program in some examples.

Notice that the Haskell definition is the same as the mathematical

one, provided we define all the other needed ingredients:

> least :: (Natural -> Bool) -> Natural > least p = if p 0 then 0 else 1 + least(\n -> p(n+1)) > (-->) :: Bool -> Bool -> Bool > p --> q = not p || q > eq :: Natural -> Cantor -> Cantor -> Bool > eq 0 a b = True > eq (n+1) a b = a n == b n && eq n a b

To understand the modulus functional in practice, define projections

as follows:

> proj :: Natural -> (Cantor -> Integer) > proj i = \a -> coerce(a i)

Then we get:

*Main> modulus (\a -> 45000) 0 (0.00 secs, 0 bytes) *Main> modulus (proj 1) 2 (0.00 secs, 0 bytes) *Main> modulus (proj 2) 3 (0.01 secs, 0 bytes) *Main> modulus (proj 3) 4 (0.05 secs, 820144 bytes) *Main> modulus (proj 4) 5 (0.30 secs, 5173540 bytes) *Main> modulus (proj 5) 6 (1.69 secs, 31112400 bytes) *Main> modulus (proj 6) 7 (9.24 secs, 171456820 bytes)

So, intuitively, the modulus is the last index of the input that the

function uses plus one. For a constant function, like the above, the

modulus is zero, because no index is used.

**Technical remark**. The notion of modulus of uniform

continuity needed for the proof of termination of `find_i`

is not literally the same as above, but a slight variant

(sometimes called the *intensional* modulus of uniform

continuity, whereas ours is referred to as the

*extensional* one). But I won’t go into such mathematical

subtleties here. The main idea is that when the modulus is $0$ the

recursion terminates and one of the branches of the definition of

`find_i` is followed, and a new recursion is started, to

produce the next digit of the example. When the modulus of

`p` is $n+1$, the modulus of the predicate `\a -> p(Zero #
a)` is $n$ or smaller, and so recursive calls are always made

with smaller moduli and hence eventually terminate.

**End of**

remark.

remark.

Now I’ll try to get faster implementations of `find`.

I’ll modify the original implementation in several stages.

Firstly, I will remove the mutual recursion by expanding the

definition of the function `forsome` in the definition of

the function `find_i`:

> find_ii p = if p(Zero # find_ii(\a -> p(Zero # a))) > then Zero # find_ii(\a -> p(Zero # a)) > else One # find_ii(\a -> p(One # a))

This should have essentially the same speed.

Now notice that the branches of the conditional are the same if we

make zero and one into a parameter `h`. Hence one can “factor

out” the conditional as follows:

> find_iii :: (Cantor -> Bool) -> Cantor > find_iii p = h # find_iii(\a -> p(h # a)) > where h = if p(Zero # find_iii(\a -> p(Zero # a))) then Zero else One

This is (exponentially!) faster for some examples. A clue for

this is that `h` will be evaluated only if and when it is

“used” (our language is lazy). Let’s run an example, replacing the

above definition of `find` by `find = find_iii`:

*Main> equal f h True (0.00 secs, 522668 bytes) *Main> equal (proj 1000) (proj 1000) True (0.00 secs, 0 bytes) *Main> equal (proj 1000) (proj 4000) False (0.03 secs, 1422680 bytes) *Main> equal (proj 1000) (proj(2^20)) False (7.02 secs, 336290704 bytes)

As you can see, the bigger the projection functions we try,

the longer the comparison gets. To see how bad the first algorithm is, let’s switch back to `find = find_i`:

*Main> equal (proj 10) (proj 10) True (0.05 secs, 1529036 bytes) *Main> equal (proj 10) (proj 15) False (1.61 secs, 72659036 bytes) *Main> equal (proj 10) (proj 20) False (60.62 secs, 2780497676 bytes)

The previous examples cannot be run with this algorithm unless we had

more bits available than there are atoms in the observable universe

and we were willing to wait several billion-billion years, because the

algorithm is exponential in the modulus of continuity.

You probably noticed that there is another obvious improvement

starting from `find_ii`:

> find_iv :: (Cantor -> Bool) -> Cantor > find_iv p = let leftbranch = Zero # find_iv(\a -> p(Zero # a)) > in if p(leftbranch) > then leftbranch > else One # find_iv(\a -> p(One # a))

Actually, I never thought about the performance of this algorithm or

experimented with it. Let’s see what we get (you need to replace

`find = find_iv`):

*Main> equal (proj 10) (proj 20) False (0.00 secs, 522120 bytes) *Main> equal (proj 10) (proj 200) False (0.04 secs, 1550824 bytes) *Main> equal (proj 10) (proj 2000) False (3.71 secs, 146039744 bytes) *Main> equal (proj 10) (proj 20000) Interrupted.

Much better than `find_i`, but much worse than

`find_iii`! I gave up in the last example, because it

started to slow down my edition of this post after a minute or so.

But there is a much better algorithm, which I now present. I won’t

attempt to explain the working of this algorithm in this post (see

my LICS’2007 paper below if you are really interested), but I include

a few remarks below:

> find_v :: (Cantor -> Bool) -> Cantor > find_v p = \n -> if q n (find_v(q n)) then Zero else One > where q n a = p(\i -> if i < n then find_v p i else if i == n then Zero else a(i-n-1))

All the above algorithms, except this one, can be easily rewritten to

use lazy lists rather than functions defined on the natural

numbers. This algorithm takes advantage of the fact that to access an

element of a sequence represented as a function, it is not necessary

to scan all the preceding elements. In a perhaps mysterious way, this

algorithm implicitly figures out which entries of its argument

`p` uses, and constructs only those explicitly. You can access

the other ones if you wish, but the algorithm `find_v` doesn’t

force their evaluation. One way to see that `find_v` is correct

is to show, by induction on `n`, that `find_i p n = find_v p
n`, which is not too difficult, although the calculations get big

at some stages if one doesn’t carefully introduce suitable auxiliary

notation. A better way is to understand this directly, as done in the

above paper (you need to look for the product functional, which

generalizes this).

Now this gets really fast (take `find = find_v`):

*Main> equal (proj (2^300)) (proj (2^300)) True (0.00 secs, 522148 bytes) *Main> equal (proj (2^300)) (proj (2^400)) False (0.00 secs, 525064 bytes)

But if the functions use several of their arguments, not just one (see

example below), this isn’t so good any more. To fix this, first

rewrite the above program as follows, introducing an auxiliary

variable `b` to name the result, and replace one of the

recursive calls (there are two) to use `b` instead:

> find_vi :: (Cantor -> Bool) -> Cantor > find_vi p = b > where b = \n -> if q n (find_vi(q n)) then Zero else One > q n a = p(\i -> if i < n then b i else if i == n then Zero else a(i-n-1))

Lazy evaluation doesn’t help here, because `b` is a

function, and in fact this makes the program slightly slower. Now,

to make it significantly faster, we apply the identity function to

the definition of `b`. Or rather an elaborate

implementation of the identity function, that stores `b`

into an infinite binary tree in a breadth-first manner and then

retrieves it back (this trick implements memoization with

logarithmic overhead):

> find_vii :: (Cantor -> Bool) -> Cantor > find_vii p = b > where b = id'(\n -> if q n (find_vii(q n)) then Zero else One) > q n a = p(\i -> if i < n then b i else if i == n then Zero else a(i-n-1)) > data T x = B x (T x) (T x) > code :: (Natural -> x) -> T x > code f = B (f 0) (code(\n -> f(2*n+1))) > (code(\n -> f(2*n+2))) > decode :: T x -> (Natural -> x) > decode (B x l r) n | n == 0 = x > | odd n = decode l ((n-1) `div` 2) > | otherwise = decode r ((n-2) `div` 2) > id' :: (Natural -> x) -> (Natural -> x) > id' = decode.code

Now take `find = find_vii`, and test:

> f',g',h' :: Cantor -> Integer > f' a = a'(10*a'(3^80)+100*a'(4^80)+1000*a'(5^80)) where a' i = coerce(a i) > g' a = a'(10*a'(3^80)+100*a'(4^80)+1000*a'(6^80)) where a' i = coerce(a i) > h' a = a' k > where i = if a'(5^80) == 0 then 0 else 1000 > j = if a'(3^80) == 1 then 10+i else i > k = if a'(4^80) == 0 then j else 100+j > a' i = coerce(a i) *Main> equal f' g' False (6.75 secs, 814435692 bytes) *Main> equal f' h' True (3.20 secs, 383266912 bytes) *Main> equal g' h' False (6.79 secs, 813083216 bytes) *Main> equal f' f' True (3.20 secs, 383384908 bytes) *Main> equal g' g' True (3.31 secs, 400711084 bytes) *Main> equal h' h' True (3.22 secs, 383274252 bytes)

Among all the above algorithms, only `find_vii` can cope

with the above examples. A more interesting example is this. Two

finite sequences $s$ and $t$ of natural numbers have the

same *set* of elements iff the two functions

$\bigwedge_{i < |s|} \mathrm{proj}_{s_i}$ and $\bigwedge_{i < |t|}

\mathrm{proj}_{t_i}$

are equal, where the above notation indicates the pointwise

logical-and (conjunction) of the projections, and where $|s|$ is the

length of $s$. Here is an implementation of this

idea:

> pointwiseand :: [Natural] -> (Cantor -> Bool) > pointwiseand [] = \b -> True > pointwiseand (n:a) = \b -> (b n == One) && pointwiseand a b > sameelements :: [Natural] -> [Natural] -> Bool > sameelements a b = equal (pointwiseand a) (pointwiseand b) *Main> sameelements [6^60, 5^50, 1, 5, 6, 6, 8, 9, 3, 4, 6, 2, 4,6, 1, 6^60, 7^700, 1, 1, 1, 3, 3^30] [1, 2, 3, 4, 5, 6, 7, 8, 9, 3^30, 5^50, 6^60, 7^70] False (0.14 secs, 21716248 bytes) *Main> sameelements [6^60, 5^50, 1, 5, 6, 6, 8, 9, 3, 4, 6, 2, 4,6, 1, 6^60, 7^70, 1, 1, 1, 3, 3^30] [1, 2, 3, 4, 5, 6, 7, 8, 9, 3^30, 5^50, 6^60, 7^70] False (0.10 secs, 14093520 bytes) *Main> sameelements [6^60, 5^50, 1, 5, 6, 6, 8, 9, 3, 4, 6, 2, 4,6, 1, 6^60, 7^70, 1, 1, 1, 3, 3^30] [1, 2, 3, 4, 5, 6, 8, 9, 3^30, 5^50, 6^60, 7^70] True (0.10 secs, 12610056 bytes) *Main> sameelements [6^60, 5^50, 1, 5, 6, 6, 8, 9, 3, 4, 6, 2, 4,6, 1, 6^60, 7^70, 1, 1, 1, 3, 3^30] [1, 2, 3, 4, 5, 6, 8, 9, 3^30, 5^50, 6^60, 7^700] False (0.12 secs, 17130684 bytes) *Main> sameelements [6^60, 5^50, 1, 5, 6, 6, 8, 9, 3, 4, 6, 2, 4,6, 1, 6^60, 7^700, 1, 1, 1, 3, 3^30] [1, 2, 3, 4, 5, 6, 8, 9, 3^30, 5^50, 6^60, 7^700] True (0.12 secs, 17604776 bytes)

It is natural to ask whether there are applications to program

verification. I don’t know, but Dan Ghica and I speculate that

there are, and we are planning to investigate this.

An even faster search algorithm is offered in the first comment below.

### References with comments

- Ulrich Berger.
**Totale Objekte und Mengen in der Bereichtheorie**. PhD thesis, Munich LMU, 1990. - M.H. Escardo.
**Infinite sets that admit fast exhaustive**. In LICS’2007, Poland, Wroclaw, July.

search

Download companion Haskell program.This paper investigates which kinds of infinite sets admit exhaustive

search. It gives several algorithms for systematically building new

searchable sets from old. It also shows that, for a rich collection of

types, any subset that admits a quantifier also admits a searcher. The

algorithm for constructing the searcher from the quantifier is slow,

and so at present this result is of theoretical interest only. But the

other algorithms are fast. - M.H. Escardo.
**Synthetic topology of data types and**. ENTCS, Elsevier, volume 87, pages 21-156, November

classical spaces

2004.I would nowadays call this algorithmic topology of program types,

rather than synthetic topology of data types, and in future writings

this is how I’ll refer to it. It shows how notions and theorems from

general topology can be directly mapped into programming languages (I

use Haskell again). But it also shows how this can be mapped back to

classical topology. Exhaustively searchable sets feature there as

computational manifestations of compact sets. - M.H. Escardo and W.K. Ho.
**Operational domain theory**.

and topology of a sequential programming language

In Proceedings of the 20th Annual IEEE Symposium on Logic in Computer Science (LICS), June 2005, pages 427-436.If you want to learn a bit about the use of domain theory and topology

for reasoning about programs, this is a possible starting point.

Rather than defining a denotational semantics using domain theory and

topology, we extract them directly from the operational semantics of

the language (PCF, which can be regarded as a faithful subset of

Haskell), side-stepping denotational semantics. Many definitions of

domain theory and denotational semantics arise as theorems here. There

is a proof of Berger’s program, including the appropriate notion of

modulus of uniform continuity needed for the proof. - Dag Normann.
**Computing with functionals – computability theory or computer science?**. Bulletin of Symbolic Logic, 12(1):43-59, 2006.This is a nice short history and survey of higher-type computability

theory (the subject was established in the late 1950’s by work of

Kleene and Kreisel, but had some precursors). This theory is not well

known among functional programmers, but it probably should be, at

least among those with theoretical inclinations. Somebody someday

should write an expository account to computer scientists. - Alex Simpson.
**Lazy Functional Algorithms for Exact Real Functionals**. In*Mathematical Foundations of Computer Science 1998*,

Springer LNCS 1450, pp. 456-464, 1998.One can use infinite sequences to represent real numbers exactly.

Using the universal quantification functional, Alex Simpson developed

an algorithm for Riemann integration. The algorithm turns out to be

inefficient, but this is not the universal quantifier’s fault: the

Haskell profiler tells me that the integration program performs about

10000 universal quantifications per second, 3000 of which return

`True`.

I’ve found a way of making the fastest algorithm about 40 faster by avoiding the trees and instead treating functions as trees:

> find = find_viii

> findBit :: (Bit -> Bool) -> Bit

> findBit p = if p Zero then Zero else One

> branch :: Bit -> Cantor -> Cantor -> Cantor

> branch x l r n | n == 0 = x

> | odd n = l ((n-1) `div` 2)

> | otherwise = r ((n-2) `div` 2)

> find_viii :: (Cantor -> Bool) -> Cantor

> find_viii p = branch x l r

> where x = findBit(\x -> forsome(\l -> forsome(\r -> p(branch x l r))))

> l = find_viii(\l -> forsome(\r -> p(branch x l r)))

> r = find_viii(\r -> p(branch x l r))

This doesn’t need the memoization, because the things one needs to remember are bound to variables in the where-clause. Using this algorithm, comparing f’ and g’, and f’ and h’ for equality moves from 6.75 and 3.20 seconds to respectively 0.14 and 0.09 seconds (using the Glasgow interpreter in all cases).

Martin, this whole post was fascinating but even so those last two sentences pack the biggest punch of all. Where should I look for the follow-up?

The promised follow-up blog post hasn’t been written, although there is a related one at

http://math.andrej.com/2008/11/21/a-haskell-monad-for-infinite-search-in-finite-time/

There are, however, a couple of papers that develop/apply these ideas, available from my web page, written after this post:

1. Exhaustible sets in higher-type computation

2. (With P. Oliva) Selection functions, bar recursion, and backward induction.

3. Semi-decidability of may, must and probabilistic testing in a higher-type setting.

4. Computability of continuous solutions of higher-type equations.

Call me crazy, but I took the “You can’t do this in Java” bet.

Here it comes: The seemingly impossible Java-implementation of a seemingly impossible functional problem:

https://github.com/untoldwind/seemingly_impossible

Ok, I just implemented the first three find’s and I admit that this implementation is a sure way to exhaust the Java-Stack quite easily (if you get a StackOverflow running this stuff you might have to use -Xss512m or something).

For completeness I’ve added my own find-variant (FindJ), i.e. how would you solve this little problem in “regular Java”, whatever that means (*cough* it’s doing the examples above in <1ms *cough*).

Now, before the tomatoes fly: This is a hack! Also this is

notabout “my language is better than yours”. Functional programming languages have their appeal and are able so solve certain types of problems in a very natural way, the Java-implementation is a syntactic mess in compassion.But: I seriously doubt that there are problems you can only solve with a functional programming language.

After all the Haskell compiler can not do magic, even though it might seem so sometimes.

Do these work for functions that have state? To state precisely what’s wanted one would have to be careful about extensionality (although you glossed over it, just as you need to be careful about totality in the pure functional setting).

[Even if the particular programs above don’t work with stateful functions, the Kreisel-Lacombe-Shoenfield theorem makes one think that it is possible to do the same thing for stateful functions.]

@Ralph: This post is written in the context of purely functional programs, i.e., no state, and all functionals are presumed to be (hereditarily) total. With state things are more complicated (global state, local state, what does it mean for an extensional functional to use state?).

Yes, everything here is extensional and total, as Andrej says, and there are no effects. But Andrej gave a nice talk in MAP’2011 in the Lorentz Center showing how to do it faster with effects. But in this case, as you say, it is difficult to treat extensionality rigorously.

It turns out that you can give a sensible, negative, answer to my question. find can be parametrized with a monad pretty easily e.g., [conflating Bit and Bool to save typing].

never returns.

@Martin: my talk about faster functionals using effects is on line.

Martin, please consider answering the following question: http://cs.stackexchange.com/q/45683/37997

Thanks in advance for attention :-)