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Why Functional Programming Matters
John Hughes, Institutionen för Datavetenskap,
Chalmers Tekniska Högskola,
41296 Göteborg,
SWEDEN. rjmh@cs.chalmers.se
This paper dates from 1984, and circulated as a Chalmers memo for many
years. Slightly revised versions appeared in 1989 and 1990 as [Hug90] and
[Hug89]. This version is based on the original Chalmers memo nroff
source, lightly edited for LaTeX and to bring it closer to the published versions, and with one or two errors corrected. Please excuse the slightly oldfashioned type-setting, and the fact that the examples are not in Haskell!
Abstract
As software becomes more and more complex, it is more and more
important to structure it well. Well-structured software is easy to write,
easy to debug, and provides a collection of modules that can be re-used
to reduce future programming costs. Conventional languages place conceptual limits on the way problems can be modularised. Functional languages push those limits back. In this paper we show that two features of
functional languages in particular, higher-order functions and lazy evaluation, can contribute greatly to modularity. As examples, we manipulate lists and trees, program several numerical algorithms, and implement
the alpha-beta heuristic (an algorithm from Artificial Intelligence used in
game-playing programs). Since modularity is the key to successful programming, functional languages are vitally important to the real world.
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Introduction
This paper is an attempt to demonstrate to the “real world” that functional
programming is vitally important, and also to help functional programmers
exploit its advantages to the full by making it clear what those advantages are.
Functional programming is so called because a program consists entirely of
functions. The main program itself is written as a function which receives the
program’s input as its argument and delivers the program’s output as its result.
Typically the main function is defined in terms of other functions, which in
turn are defined in terms of still more functions, until at the bottom level the
functions are language primitives. These functions are much like ordinary mathematical functions, and in this paper will be defined by ordinary equations. Our
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notation follows Turner’s language Miranda(TM) [Tur85], but should be readable with no prior knowledge of functional languages. (Miranda is a trademark
of Research Software Ltd.)
The special characteristics and advantages of functional programming are
often summed up more or less as follows. Functional programs contain no
assignment statements, so variables, once given a value, never change. More
generally, functional programs contain no side-effects at all. A function call can
have no effect other than to compute its result. This eliminates a major source
of bugs, and also makes the order of execution irrelevant – since no side-effect
can change the value of an expression, it can be evaluated at any time. This
relieves the programmer of the burden of prescribing the flow of control. Since
expressions can be evaluated at any time, one can freely replace variables by
their values and vice versa – that is, programs are “referentially transparent”.
This freedom helps make functional programs more tractable mathematically
than their conventional counterparts.
Such a catalogue of “advantages” is all very well, but one must not be surprised if outsiders don’t take it too seriously. It says a lot about what functional
programming is not (it has no assignment, no side effects, no flow of control) but
not much about what it is. The functional programmer sounds rather like a medieval monk, denying himself the pleasures of life in the hope that it will make
him virtuous. To those more interested in material benefits, these “advantages”
are not very convincing.
Functional programmers argue that there are great material benefits – that
a functional programmer is an order of magnitude more productive than his
conventional counterpart, because functional programs are an order of magnitude shorter. Yet why should this be? The only faintly plausible reason one
can suggest on the basis of these “advantages” is that conventional programs
consist of 90% assignment statements, and in functional programs these can be
omitted! This is plainly ridiculous. If omitting assignment statements brought
such enormous benefits then FORTRAN programmers would have been doing it
for twenty years. It is a logical impossibility to make a language more powerful
by omitting features, no matter how bad they may be.
Even a functional programmer should be dissatisfied with these so-called
advantages, because they give him no help in exploiting the power of functional
languages. One cannot write a program which is particularly lacking in assignment statements, or particularly referentially transparent. There is no yardstick
of program quality here, and therefore no ideal to aim at.
Clearly this characterisation of functional programming is inadequate. We
must find something to put in its place – something which not only explains the
power of functional programming, but also gives a clear indication of what the
functional programmer should strive towards.
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2
An Analogy with Structured Programming
It is helpful to draw an analogy between functional and structured programming.
In the past, the characteristics and advantages of structured programming have
been summed up more or less as follows. Structured programs contain no goto
statements. Blocks in a structured program do not have multiple entries or exits.
Structured programs are more tractable mathematically than their unstructured
counterparts. These “advantages” of structured programming are very similar in
spirit to the “advantages” of functional programming we discussed earlier. They
are essentially negative statements, and have led to much fruitless argument
about “essential gotos” and so on.
With the benefit of hindsight, it is clear that these properties of structured
programs, although helpful, do not go to the heart of the matter. The most important difference between structured and unstructured programs is that structured programs are designed in a modular way. Modular design brings with
it great productivity improvements. First of all, small modules can be coded
quickly and easily. Secondly, general purpose modules can be re-used, leading to
faster development of subsequent programs. Thirdly, the modules of a program
can be tested independently, helping to reduce the time spent debugging.
The absence of gotos, and so on, has very little to do with this. It helps with
“programming in the small”, whereas modular design helps with “programming
in the large”. Thus one can enjoy the benefits of structured programming in
FORTRAN or assembly language, even if it is a little more work.
It is now generally accepted that modular design is the key to successful programming, and languages such as Modula-II [Wir82], Ada [oD80] and Standard
ML [MTH90] include features specifically designed to help improve modularity.
However, there is a very important point that is often missed. When writing
a modular program to solve a problem, one first divides the problem into subproblems, then solves the sub-problems and combines the solutions. The ways
in which one can divide up the original problem depend directly on the ways
in which one can glue solutions together. Therefore, to increase ones ability
to modularise a problem conceptually, one must provide new kinds of glue in
the programming language. Complicated scope rules and provision for separate
compilation only help with clerical details; they offer no new conceptual tools
for decomposing problems.
One can appreciate the importance of glue by an analogy with carpentry.
A chair can be made quite easily by making the parts – seat, legs, back etc. and sticking them together in the right way. But this depends on an ability
to make joints and wood glue. Lacking that ability, the only way to make a
chair is to carve it in one piece out of a solid block of wood, a much harder
task. This example demonstrates both the enormous power of modularisation
and the importance of having the right glue.
Now let us return to functional programming. We shall argue in the remainder of this paper that functional languages provide two new, very important
kinds of glue. We shall give many examples of programs that can be modularised in new ways, and thereby greatly simplified. This is the key to functional
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programming’s power – it allows greatly improved modularisation. It is also the
goal for which functional programmers must strive – smaller and simpler and
more general modules, glued together with the new glues we shall describe.
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Glueing Functions Together
The first of the two new kinds of glue enables simple functions to be glued
together to make more complex ones. It can be illustrated with a simple listprocessing problem – adding up the elements of a list. We define lists by
listof X ::= nil | cons X (listof X)
which means that a list of Xs (whatever X is) is either nil, representing a list
with no elements, or it is a cons of an X and another list of Xs. A cons represents
a list whose first element is the X and whose second and subsequent elements
are the elements of the other list of Xs. X here may stand for any type – for
example, if X is “integer” then the definition says that a list of integers is either
empty or a cons of an integer and another list of integers. Following normal
practice, we will write down lists simply by enclosing their elements in square
brackets, rather than by writing conses and nils explicitly. This is simply a
shorthand for notational convenience. For example,
[]
[1]
[1,2,3]
means
means
means
nil
cons 1 nil
cons 1 (cons 2 (cons 3 nil))
The elements of a list can be added up by a recursive function sum. Sum must
be defined for two kinds of argument: an empty list (nil), and a cons. Since the
sum of no numbers is zero, we define
sum nil = 0
and since the sum of a cons can be calculated by adding the first element of the
list to the sum of the others, we can define
sum (cons num list) = num + sum list
Examining this definition, we see that only the boxed parts below are specific
to computing a sum.
+—+
sum nil = | 0 |
+—+
+—+
sum (cons num list) = num | + | sum list
+—+
This means that the computation of a sum can be modularised by glueing
together a general recursive pattern and the boxed parts. This recursive pattern
is conventionally called reduce and so sum can be expressed as
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sum = reduce add 0
where for convenience reduce is passed a two argument function add rather than
an operator. Add is just defined by
add x y = x + y
The definition of reduce can be derived just by parameterising the definition of
sum, giving
(reduce f x) nil = x
(reduce f x) (cons a l) = f a ((reduce f x) l)
Here we have written brackets around (reduce f x) to make it clear that it
replaces sum. Conventionally the brackets are omitted, and so ((reduce f x) l) is
written as (reduce f x l). A function of 3 arguments such as reduce, applied to
only 2 is taken to be a function of the one remaining argument, and in general,
a function of n arguments applied to only m(< n) is taken to be a function of the n − m remaining ones. We will follow this convention in future. Having modularised sum in this way, we can reap benefits by re-using the parts. The most interesting part is reduce, which can be used to write down a function for multiplying together the elements of a list with no further programming: product = reduce multiply 1 It can also be used to test whether any of a list of booleans is true anytrue = reduce or false or whether they are all true alltrue = reduce and true One way to understand (reduce f a) is as a function that replaces all occurrences of cons in a list by f, and all occurrences of nil by a. Taking the list [1,2,3] as an example, since this means cons 1 (cons 2 (cons 3 nil)) then (reduce add 0) converts it into add 1 (add 2 (add 3 0)) = 6 and (reduce multiply 1) converts it into multiply 1 (multiply 2 (multiply 3 1)) = 6 Now it is obvious that (reduce cons nil) just copies a list. Since one list can be appended to another by consing its elements onto the front, we find append a b = reduce cons b a 5 As an example, append [1,2] [3,4] = reduce cons [3,4] [1,2] = (reduce cons [3,4]) (cons 1 (cons 2 nil)) = cons 1 (cons 2 [3,4])) (replacing cons by cons and nil by [3,4]) = [1,2,3,4] A function to double all the elements of a list could be written as where doubleall = reduce doubleandcons nil doubleandcons num list = cons (2*num) list Doubleandcons can be modularised even further, first into where doubleandcons = fandcons double double n = 2*n fandcons f el list = cons (f el) list and then by fandcons f = cons . f where “.” (function composition, a standard operator) is defined by (f . g) h = f (g h) We can see that the new definition of fandcons is correct by applying it to some arguments: so fandcons f el = (cons . f) el = cons (f el) fandcons f el list = cons (f el) list The final version is doubleall = reduce (cons . double) nil With one further modularisation we arrive at doubleall = map double map f = reduce (cons . f) nil where map applies any function f to all the elements of a list. Map is another generally useful function. We can even write down a function to add up all the elements of a matrix, represented as a list of lists. It is summatrix = sum . map sum 6 The map sum uses sum to add up all the rows, and then the left-most sum adds up the row totals to get the sum of the whole matrix. These examples should be enough to convince the reader that a little modularisation can go a long way. By modularising a simple function (sum) as a combination of a “higher order function” and some simple arguments, we have arrived at a part (reduce) that can be used to write down many other functions on lists with no more programming effort. We do not need to stop with functions on lists. As another example, consider the datatype of ordered labelled trees, defined by treeof X ::= node X (listof (treeof X)) This definition says that a tree of Xs is a node, with a label which is an X, and a list of subtrees which are also trees of Xs. For example, the tree 1 o / \ / \ / \ 2 o o 3 | | | o 4 would be represented by node 1 (cons (node 2 nil) (cons (node 3 (cons (node 4 nil) nil)) nil)) Instead of considering an example and abstracting a higher order function from it, we will go straight to a function redtree analogous to reduce. Recall that reduce took two arguments, something to replace cons with, and something to replace nil with. Since trees are built using node, cons and nil, redtree must take three arguments - something to replace each of these with. Since trees and lists are of different types, we will have to define two functions, one operating on each type. Therefore we define redtree f g a (node label subtrees) = f label (redtree’ f g a subtrees) redtree’ f g a (cons subtree rest) = g (redtree f g a subtree) (redtree’ f g a rest) redtree’ f g a nil = a Many interesting functions can be defined by glueing redtree and other functions together. For example, all the labels in a tree of numbers can be added together using 7 sumtree = redtree add add 0 Taking the tree we wrote down earlier as an example, sumtree gives add 1 (add (add 2 0) (add (add 3 (add (add 4 0) 0)) 0)) = 10 A list of all the labels in a tree can be computed using labels = redtree cons append nil The same example gives cons 1 (append (cons 2 nil) (append (cons 3 (append (cons 4 nil) nil)) nil)) = [1,2,3,4] Finally, one can define a function analogous to map which applies a function f to all the labels in a tree: maptree f = redtree (node . f) cons nil All this can be achieved because functional languages allow functions which are indivisible in conventional programming languages to be expressed as a combination of parts - a general higher order function and some particular specialising functions. Once defined, such higher order functions allow many operations to be programmed very easily. Whenever a new datatype is defined higher order functions should be written for processing it. This makes manipulating the datatype easy, and also localises knowledge about the details of its representation. The best analogy with conventional programming is with extensible languages - it is as though the programming language can be extended with new control structures whenever desired. 4 Glueing Programs Together The other new kind of glue that functional languages provide enables whole programs to be glued together. Recall that a complete functional program is just a function from its input to its output. If f and g are such programs, then (g . f) is a program which, when applied to its input, computes g (f input) 8 The program f computes its output which is used as the input to program g. This might be implemented conventionally by storing the output from f in a temporary file. The problem with this is that the temporary file might occupy so much memory that it is impractical to glue the programs together in this way. Functional languages provide a solution to this problem. The two programs f and g are run together in strict synchronisation. F is only started once g tries to read some input, and only runs for long enough to deliver the output g is trying to read. Then f is suspended and g is run until it tries to read another input. As an added bonus, if g terminates without reading all of f’s output then f is aborted. F can even be a non-terminating program, producing an infinite amount of output, since it will be terminated forcibly as soon as g is finished. This allows termination conditions to be separated from loop bodies - a powerful modularisation. Since this method of evaluation runs f as little as possible, it is called “lazy evaluation”. It makes it practical to modularise a program as a generator which constructs a large number of possible answers, and a selector which chooses the appropriate one. While some other systems allow programs to be run together in this manner, only functional languages use lazy evaluation uniformly for every function call, allowing any part of a program to be modularised in this way. Lazy evaluation is perhaps the most powerful tool for modularisation in the functional programmer’s repertoire. 4.1 Newton-Raphson Square Roots We will illustrate the power of lazy evaluation by programming some numerical algorithms. First of all, consider the Newton-Raphson algorithm for finding square roots. This algorithm computes the square root of a number N by starting from an initial approximation a0 and computing better and better ones using the rule a(n+1) = (a(n) + N/a(n)) / 2 If the approximations converge to some limit a, then so a = (a + N/a) / 2 2a = a + N/a a = N/a a*a = N a = squareroot(N) In fact the approximations converge rapidly to a limit. Square root programs take a tolerance (eps) and stop when two successive approximations differ by less than eps. The algorithm is usually programmed more or less as follows: C N IS CALLED ZN HERE SO THAT IT HAS THE RIGHT TYPE X = A0 Y = A0 + 2.*EPS 9 C THE VALUE OF Y DOES NOT MATTER SO LONG AS ABS(X-Y).GT.EPS 100 IF (ABS(X-Y).LE.EPS) GOTO 200 Y = X X = (X + ZN/X) / 2. GOTO 100 200 CONTINUE C THE SQUARE ROOT OF ZN IS NOW IN X This program is indivisible in conventional languages. We will express it in a more modular form using lazy evaluation, and then show some other uses to which the parts may be put. Since the Newton-Raphson algorithm computes a sequence of approximations it is natural to represent this explicitly in the program by a list of approximations. Each approximation is derived from the previous one by the function next N x = (x + N/x) / 2 so (next N) is the function mapping one approximation onto the next. Calling this function f, the sequence of approximations is [a0, f a0, f(f a0), f(f(f a0)), ..] We can define a function to compute this: repeat f a = cons a (repeat f (f a)) so that the list of approximations can be computed by repeat (next N) a0 Repeat is an example of a function with an “infinite” output - but it doesn’t matter, because no more approximations will actually be computed than the rest of the program requires. The infinity is only potential: all it means is that any number of approximations can be computed if required, repeat itself places no limit. The remainder of a square root finder is a function within, that takes a tolerance and a list of approximations and looks down the list for two successive approximations that differ by no more than the given tolerance. It can be defined by within eps (cons a (cons b rest)) = = b, if abs(a-b) number Since a game-tree is a (treeof position), it can be converted into a (treeof number) by the function (maptree static), which statically evaluates all the positions in the tree (which may be infinitely many). This uses the function maptree defined in section 2. 16 | | -+-+gametree | | -+-+| | = | | -+-+| | -+-+| | / | \ / | \ / | \ / | \ / | \ / | \ X| | |X| | | -+-+-+-+-+-+| | | | |X| -+-+-+-+-+-+| | | | | | /|\ /|\ /\ ... ... / \ / \ / \ O| | |O| -+-+-+-+|X| |X| -+-+-+-+| | | | /|\ /|\ ... ... Figure 1: An Example of a Game-Tree. 17 Given such a tree of static evaluations, what is the true value of the positions in it? In particular, what value should be ascribed to the root position? Not its static value, since this is only a rough guess. The value ascribed to a node must be determined from the true values of its subnodes. This can be done by assuming that each player makes the best moves he can. Remembering that a high value means a good position for the computer, it is clear that when it is the computer’s move from any position, it will choose the move leading to the sub-node with the maximum true value. Similarly, the opponent will choose the move leading to the sub-node with the minimum true value. Assuming that the computer and its opponent alternate turns, the true value of a node is computed by the function maximise if it is the computer’s turn and minimise if it is not: maximise (node n sub) = max (map minimise sub) minimise (node n sub) = min (map maximise sub) Here max and min are functions on lists of numbers that return the maximum and minimum of the list respectively. These definitions are not complete because they recurse for ever - there is no base case. We must define the value of a node with no successors, and we take it to be the static evaluation of the node (its label). Therefore the static evaluation is used when either player has already won, or at the limit of look-ahead. The complete definitions of maximise and minimise are maximise (node n nil) = n maximise (node n sub) = max (map minimise sub) minimise (node n nil) = n minimise (node n sub) = min (map maximise sub) One could almost write down a function at this stage that would take a position and return its true value. This would be: evaluate = maximise . maptree static . gametree There are two problems with this definition. First of all, it doesn’t work for infinite trees. Maximise keeps on recursing until it finds a node with no subtrees - an end to the tree. If there is no end then maximise will return no result. The second problem is related - even finite game trees (like the one for noughts and crosses) can be very large indeed. It is unrealistic to try to evaluate the whole of the game tree - the search must be limited to the next few moves. This can be done by pruning the tree to a fixed depth, prune 0 (node a x) = node a nil prune n (node a x) = node a (map (prune (n-1)) x) (prune n) takes a tree and “cuts off” all nodes further than n from the root. If a game tree is pruned it forces maximise to use the static evaluation for nodes at depth n, instead of recursing further. Evaluate can therefore be defined by evaluate = maximise . maptree static . prune 5 . gametree 18 which looks (say) 5 moves ahead. Already in this development we have used higher-order functions and lazy evaluation. Higher order functions reptree and maptree allow us to construct and manipulate game trees with ease. More importantly, lazy evaluation permits us to modularise evaluate in this way. Since gametree has a potentially infinite result, this program would never terminate without lazy evaluation. Instead of writing prune 5 . gametree we would have to fold these two functions together into one which only constructed the first five levels of the tree. Worse, even the first five levels may be too large to be held in memory at one time. In the program we have written, the function maptree static . prune 5 . gametree only constructs parts of the tree as maximise requires them. Since each part can be thrown away (reclaimed by the garbage collector) as soon as maximise has finished with it, the whole tree is never resident in memory. Only a small part of the tree is stored at a time. The lazy program is therefore efficient. Since this efficiency depends on an interaction between maximise (the last function in the chain of compositions) and gametree (the first), it could only be achieved without lazy evaluation by folding all the functions in the chain together into one big one. This is a drastic reduction in modularity, but it is what is usually done. We can make improvements to this evaluation algorithm by tinkering with each part: this is relatively easy. A conventional programmer must modify the entire program as a unit, which is much harder. So far we have only described simple minimaxing. The heart of the alphabeta algorithm is the observation that one can often compute the value of maximise or minimise without looking at the whole tree. Consider the tree: max / \ / \ / \ / \ min min / \ / \ / \ / \ 1 2 0 ? Strangely enough, it is unnecessary to know the value of the question mark in order to evaluate the tree. The left minimum evaluates to 1, but the right minimum clearly evaluates to something less than or equal to 0. Therefore the maximum of the two minima must be 1. This observation can be generalised and built into maximise and minimise. The first step is to separate maximise into an application of max to a list of numbers; that is, we decompose maximise as 19 maximise = max . maximise’ (Minimise is decomposed in a similar way. Since minimise and maximise are entirely symmetrical we shall discuss maximise and assume that minimise is treated similarly). Once decomposed in this way, maximise can use minimise’ rather than minimise itself, to discover which numbers minimise would take the minimum of. It may then be able to discard some of the numbers without looking at them. Thanks to lazy evaluation, if maximise doesn’t look at all of the list of numbers, some of them will not be computed, with a potential saving in computer time. It is easy to “factor out” max from the definition of maximise, giving maximise’ (node n nil) = cons n nil maximise’ (node n l) = map minimise l = map (min . minimise’) l = map min (map minimise’ l) = mapmin (map minimise’ l) where mapmin = map min Since minimise’ returns a list of numbers, the minimum of which is the result of minimise, (map minimise’ l) returns a list of lists of numbers. Maximise’ should return a list of the minima of those lists. However, only the maximum of this list matters. We shall define a new version of mapmin which omits the minima of lists whose minimum doesn’t matter. mapmin (cons nums rest) = = cons (min nums) (omit (min nums) rest) The function omit is passed a “potential maximum” - the largest minimum seen so far - and omits any minima which are less than this. omit pot nil = nil omit pot (cons nums rest) = = omit pot rest, if minleq nums pot = cons (min nums) (omit (min nums) rest), otherwise Minleq takes a list of numbers and a potential maximum, and returns true if the minimum of the list of numbers is less than or equal to the potential maximum. To do this, it does not need to look at all the list! If there is any element in the list less than or equal to the potential maximum, then the minimum of the list is sure to be. All elements after this particular one are irrelevant - they are like the question mark in the example above. Therefore minleq can be defined by minleq nil pot = false minleq (cons num rest) pot = true, if numn2 where sort is a general purpose sorting function. The evaluator would now be defined by evaluate = max . maximise’ . highfirst . maptree static . prune 8 . gametree One might regard it as sufficient to consider only the three best moves for the computer or the opponent, in order to restrict the search. To program this, it is only necessary to replace highfirst with (taketree 3 . highfirst), where taketree n = redtree (nodett n) cons nil nodett n label sub = node label (take n sub) Taketree replaces all the nodes in a tree with nodes with at most n subnodes, using the function (take n) which returns the first n elements of a list (or fewer if the list is shorter than n). Another improvement is to refine the pruning. The program above looks ahead a fixed depth even if the position is very dynamic - it may decide to look no further than a position in which the queen is threated in chess, for example. It is usual to define certain “dynamic” positions and not to allow look-ahead to stop in one of these. Assuming a function “dynamic” that recognises such positions, we need only add one equation to prune to do this: prune 0 (node pos sub) = node pos (map (prune 0) sub), if dynamic pos Making such changes is easy in a program as modular as this one. As we remarked above, since the program depends crucially for its efficiency on an interaction between maximise, the last function in the chain, and gametree, the first, it can only be written as a monolithic program without lazy evaluation. Such a program is hard to write, hard to modify, and very hard to understand. 21 6 Conclusion In this paper, we’ve argued that modularity is the key to successful programming. Languages which aim to improve productivity must support modular programming well. But new scope rules and mechanisms for separate compilation are not enough - modularity means more than modules. Our ability to decompose a problem into parts depends directly on our ability to glue solutions together. To assist modular programming, a language must provide good glue. Functional programming languages provide two new kinds of glue - higher-order functions and lazy evaluation. Using these glues one can modularise programs in new and exciting ways, and we’ve shown many examples of this. Smaller and more general modules can be re-used more widely, easing subsequent programming. This explains why functional programs are so much smaller and easier to write than conventional ones. It also provides a target for functional programmers to aim at. If any part of a program is messy or complicated, the programmer should attempt to modularise it and to generalise the parts. He should expect to use higher-order functions and lazy evaluation as his tools for doing this. Of course, we are not the first to point out the power and elegance of higherorder functions and lazy evaluation. For example, Turner shows how both can be used to great advantage in a program for generating chemical structures [Tur81]. Abelson and Sussman stress that streams (lazy lists) are a powerful tool for structuring programs [AS86]. Henderson has used streams to structure functional operating systems [P.H82]. The main contribution of this paper is to assert that better modularity alone is the key to the power of functional languages. It is also relevant to the present controversy over lazy evaluation. Some believe that functional languages should be lazy, others believe they should not. Some compromise and provide only lazy lists, with a special syntax for constructing them (as, for example, in SCHEME [AS86]). This paper provides further evidence that lazy evaluation is too important to be relegated to secondclass citizenship. It is perhaps the most powerful glue functional programmers possess. One should not obstruct access to such a vital tool. Acknowledgements This paper owes much to many conversations with Phil Wadler and Richard Bird in the Programming Research Group at Oxford. Magnus Bondesson at Chalmers University, Goteborg pointed out a serious error in an earlier version of one of the numerical algorithms, and thereby prompted development of many of the others. This work was carried out with the support of a Research Fellowship from the UK Science and Engineering Research Council. 22 References [AS86] H. Abelson and G.J. Sussman. Structure and Interpretation of Computer Programs. MIT Press, Boston, 1986. [Hug89] J. Hughes. Why Functional Programming Matters. Computer Journal, 32(2), 1989. [Hug90] John Hughes. Why Functional Programming Matters. In D. Turner, editor, Research Topics in Functional Programming. Addison Wesley, 1990. [MTH90] R. Milner, M. Tofte, and R. Harper. The Definition of Standard ML. MIT Press, 1990. [oD80] United States Department of Defense. The Programming Language Ada Reference Manual. Springer-Verlag, 1980. [P.H82] P.Henderson. Purely Functional Operating Systems. 1982. [Tur81] D. A. Turner. The Semantic Elegance of Applicative Languages. In Proceedings 1981 Conference on Functional Languages and Computer Architecture, Wentworth-by-the-Sea, Portsmouth, New Hampshire, 1981. [Tur85] D. A. Turner. Miranda: A non-strict language with polymorphic types. In Proceedings 1985 Conference on Functional Programming Languages and Computer Architecture, pages 1–16, Nancy, France, 1985. [Wir82] N. Wirth. Programming in Modula-II. Springer-Verlag, 1982. 23