This post is the first of a series, which I think may be useful for those learning functional programming.
In purely functional programming, a function is regarded as a fixed set of associations between arguments and the corresponding values.
Here is the Fibonacci sequence defined by the linear recurrence equation in Haskell:
fib :: Integer > Integer
fib 0 = 0
fib 1 = 1
fib n = fib (n1) + fib (n2)
What we have in here consists of equations, each of which establishes an equality between the left and right hand sides of the equal sign. Each case expects some input integer to conform to a given pattern, 0
, 1
or n
and outputs a Fibonacci number. fib n
runs recursively until the base case is reached and a sum is returned.
This view of a function is said to be denotational since you are constructing denotations (or meanings) to define it; in contrast to operational, where a function is seen as a sequence of operations in time.
In FP, we understand the "true meaning" of a program, because we know the mathematical object it denotes. The mathematical objects for the Haskell programs
fib 1
,1
,54
, andfib (21) + fib (22)
can be represented by the integer 1. We say that all those programs denote the integer 1.
Similarly, fib 0
, fib 1
and fib 42
, what they all have in common is that they all denote Fibonacci numbers: the meaning of the fib n
expression is the nth Fibonacci number, an element of the Fibonacci sequence.
The collection of such mathematical objects is called the semantic domain. Broadly speaking, denotational semantics is concerned with finding domains that represent what programs do.
Referential transparency
Denotational semantics is compositional. fib 2
depends on the meaning of its constituents, fib 1
and fib 0
. To achieve compositionality, an expression must be referentially transparent meaning that it can be replaced with its corresponding value without changing the program's behavior.
Example: A function that adds two numbers is referentially transparent, but a function that divides a number by another is not, simply because the divisor can be zero and division by zero is undefined. In a lot of languages (including Haskell) division by zero throws an exception (a sideeffect) since division is a partial function.
> 10 `div` 2
5
> 10 `div` 0
*** Exception: divide by zero
To turn a partial function into a total function (a function defined for all possible input values) in Haskell, we can wrap the result with a Maybe
data type.
safeDiv :: Integral a => a > a > Maybe a
safeDiv a 0 = Nothing
safeDiv a b = Just (a `div` b)
Now this gives us a total meaning. safeDiv
returns Just a number or Nothing (given 0), but it never throws an exception.
Denotational definition
So imagine that there is a magical box (like ⟦⟧) which evaluates programs into mathematical objects. You put any programmatic expression inside this box, and on the other end of the equation you get some value for it.
⟦E⟧ : V
"An expression is a syntactic object, formed according to the syntax rules of the language. A value, by contrast, is an abstract mathematical object, such as 'the number 5', or 'the function which squares its argument'." ^{1}
Take simple arithmetic expressions written in prefix notation as example:
⟦add 1 2⟧ = 1 + 2
⟦mul 2 5⟧ = 2 × 5
⟦42⟧ = 42
⟦neg 42⟧ = 42
We can define the abstract syntax of such a language in BackusNaur form:
n ∈ Int ::= ...  1  0  1  2  ...
e ∈ Exp ::= add e e
 mul e e
 neg e
 n
All expressions map to an integer eventually. Therefore we can say, the semantic domain of this language is the set of all integers ℤ.
In fact, this definition is equivalent to how Haskell's numeralsbase package encodes a numeral using the data
keyword and a type
alias.
type ℤ = Integer
  An expression that represents the structure of a numeral.
data Exp = Lit ℤ
 Neg Exp
 Add Exp Exp
 Mul Exp Exp
You can work out the rest of the implementation for evaluating those expressions in Haskell, but it's beyond the scope of this document. Essentially, you need a way of assigning a value from the domain ℤ, to each and every expression that this syntax can produce. Namely, a valuation function.
⟦Exp⟧ : Int
⟦add e1 e2⟧ = ⟦e1⟧ + ⟦e2⟧
⟦mul e1 e2⟧ = ⟦e1⟧ × ⟦e2⟧
⟦neg e⟧ = ⟦e⟧
⟦n⟧ = n
Algebraic data types
As you see, domain has a very precise meaning in denotational semantics. Haskell's data
keyword allows writing structured type definitions that describe the composition of different domains, as well.
Maybe
is actually a sum type which is defined as follows:
data Maybe a = Just a  Nothing
A type such as this is also known as an algebraic data type (ADT for short):
 Sum types are used for alternation:
A  B
meaningA
orB
but not both.  Product types are used for combination:
A B
meaningA
andB
together.
Here is an example of how a binary tree would be declared in Haskell:
data Tree = Empty
 Leaf Int
 Node Int Tree Tree
tree = Node 1 (Leaf 2) (Node 3 (Leaf 4) Empty)
Example: Move language
Move language specification is taken from Eric Walkingshaw's CS581 lecture notes.
In the following Move program, each go
command instructs a robot to move n steps in the given direction. A semicolon is used for chaining a sequence of steps.
go E 3; go N 4; go S 1;
 Each
go
command constructs a new Step, an nunit horizontal or vertical movement.  A Move is expressed as a sequence of steps separated by a semicolon.
n ∈ Nat ::= 0  1  2  ...
d ∈ Dir ::= N  S  E  W
s ∈ Step ::= go d n
m ∈ Move ::= ε  s ; m
(Epsilon stands for empty string.)
The same syntax can have many different meanings.
Finding the total distance
Here is a how a Step or a Move expression would be evaluated for summing up the total distance a robot needs to travel. In that case, the semantic domain is the set of all natural numbers ℕ.
S⟦Step⟧ : Nat
S⟦go d k⟧ = k
M⟦Move⟧ : Nat
M⟦ε⟧ = 0
M⟦s;m⟧ = S⟦s⟧ + M⟦m⟧
Moving robots around
Based on its current position on a 2d plane, a robot should be able to compute a target position to move into. In this scenario, a Move program's semantic domain is the set of all functions that map a position (x,y) pair into another one:
⟦Expr⟧ : Pos → Pos
To denote functions, we'll use a mathematical formalism which is called Lambda calculus. Without going into too much detail: λcalculus is used for explicitly expressing the basic notions of function abstraction and application. It's the world's first programming language, and arguably the simplest.
This is the complete BNF grammar of λcalculus:
e ::= x // variable
 e e // function application
 λ x . e // function definition
Lambda abstractions are expressed in the form of λx.y
, where x
is the variable of the function and y
is its body.
For practicality, I extended λcalculus to include
+
and
operators in the following example. Also in pure lambda, all functions are unary; there are no numbers, no operators, no booleans, no if/else, no loops, no self references, no recursions. But still it is sufficiently expressive to compute anything a Turing machine can compute. Go figure.
That said, here is how we evaluate Step expressions into λ terms:
S⟦Step⟧ : Pos → Pos
S⟦go N k⟧ = λ(x,y).(x,y+k)
S⟦go S k⟧ = λ(x,y).(x,y−k)
S⟦go E k⟧ = λ(x,y).(x+k,y)
S⟦go W k⟧ = λ(x,y).(x−k,y)
A Move expression is composed of Step expressions and it can be likened to a pipeline where each Step is passed the result of the one it succeeds. In the case of an empty string after a semicolon, it simply returns the position passed without making any changes.
M⟦Move⟧ : Pos → Pos
M⟦ε⟧ = λp.p
M⟦s;m⟧ = M⟦m⟧ ◦ S⟦s⟧
The ◦ operator means function composition: a higherorder function which takes one or more functions as arguments and returns a function as a result.
Functional programming is all about function composition, and we'll discover more about it in my upcoming FP primer posts. For now, it's worth noting that that this operation really is no different than adding two numerals or multiplying them. Those are both associative binary operations with identity elements; so does the composition of functions. That is, if f
, g
and h
are composable, then f ∘ (g ∘ h) = (f ∘ g) ∘ h
.
Finally, here is how Move expressions would be evaluated in Python for finding a target position:
# M⟦ε⟧ = λp.p
def empty():
def move(p):
return p
return move
# M⟦m⟧ ◦ S⟦s⟧
def compose(f, g):
def move(x):
return f(g(x))
return move
# S⟦go N k⟧ = λ(x,y).(x,y+k)
def go_n(k):
def step(pos):
x = pos[0]
y = pos[1]+k
return (x, y)
return step
# S⟦go E k⟧ = λ(x,y).(x+k,y)
def go_e(k):
def step(pos):
x = pos[0]+k
y = pos[1]
return (x, y)
return step
# go E 3; go N 4; go E 3;
run = compose(compose(go_e(3), go_n(4)), go_e(3))
initial_pos = (0,0)
target_pos = run(initial_pos)
print(target_pos)
# Output:
# (6,4)

Simon L. Peyton Jones, The implementation of functional programming languages (P29) ↩