Download Functional Programming

Functional
Programming
Yoonsik Cheon
ycheon@utep.edu
CS 3360
Chapter 15
Spring 2011
Objectives



Become familiar with the functional
programming paradigm
Know its foundation
Understand how to write programs in Haskell
2
Outline
Imperative vs. functional programming
 Functions and lambda calculus

3
Imperative Programming



Relies on modifying on a state by using a sequence of
commands
The state is modified by assignment command.
E.g., v = E or v := E.
Commands can be:





Sequenced, e.g., C1 ; C2
Conditionally executed, e.g., if
Repeated, e.g., while
Programs are a series of instructions on how to modify
the state.
Imperative (or procedural) languages, e.g., Fortran,
Pascal, and C, support this style of programming.
4
An Abstract View

A program execution can be modeled as a
sequence of state changes starting from
an initial state.
s 0  s1  s 2  …  sn
5
Functional Programming

A functional program is simply an expression, and
executing the program means evaluating the expression.




On the positive side, one can have:



No state, i.e., variables
No assignment
No sequencing and repetition
Recursive functions
Flexibility, e.g., higher-order functions
Functional languages, e.g., Lisp, Scheme, SML, Haskell,
support this style of programming.
6
Example --- Factorial
In Mathematics, factorial function, f : N  N, is defined as:
f(x) =
1
x * f(x-1)
if x = 0
otherwise
In C, factorial function written imperatively as:
int fact(int n) {
int x = 1;
while (n > 0) {
x = x * n;
n = n - 1;
}
return x;
}
7
Example --- Factorial (Cont.)
In Mathematics, factorial function, f : N  N, is defined as:
f(x) =
1
x * f(x-1)
if x = 0
otherwise
In Haskell
fact 0 = 1
fact n = n * fact (n – 1)
fact n
| n == 0 = 1
| n > 0 = n * fact (n – 1)
fact n
| n == 0 = 1
| otherwise = n * fact (n – 1)
8
Merits of Functional Programming

By avoiding variables and assignments:
 Clear
semantics (tied to mathematical objects)
 More freedom in implementation, e.g., parallelization

By the more flexible use of functions:
 Conciseness
and elegance
 Better parameterization and modularity of programs
 Convenient ways of representing infinite data
9
Problems with Functional
Programming

Some things are harder to fit into a purely
functional model, e.g.,
 Input-output
 Interactive
or continuously running programs (e.g.,
editors, process controllers)

Functional languages also correspond less
closely to current hardware, and thus
 they
can be less efficient and
 it can be hard to reason about time and space usage.
10
In Sum,

The design of the imperative languages is based
directly on the von Neumann architecture
 Efficiency
is the primary concern, rather than the
suitability of the language for software development

The design of the functional languages is based
on mathematical functions
A
solid theoretical basis that is also closer to the user,
but relatively unconcerned with the architecture of the
machines on which programs will run
11
Outline

Imperative vs. functional programming
 Functions and lambda calculus
12
Mathematical Functions
A mathematical function is a mapping of
members of one set, called the domain set,
to another set, called the range set.
1
1


f: N+  N+
domain
100

10


Range
13
Higher-Order Functions
A higher-order function, or functional form,
is one that either takes functions as
parameters or yields a function as its
result, or both.
 Examples

 Function
composition
 Construction
 Apply-to-all
14
Functional Composition

A functional form that takes two functions as
parameters and yields a function whose value is
the first actual parameter function applied to the
application of the second
Form: h  f ° g
(or g ; f)
which means h (x)  f (g ( x))
For f (x)  x * x * x and g (x)  x + 3,
h  f ° g yields (x + 3)* (x + 3)* (x + 3)
15
Construction

A functional form that takes a list of
functions as parameters and yields a list of
the results of applying each of its
parameter functions to a given parameter
Form: [|f, g|]
For f (x)  x * x * x and g (x)  x + 3,
[|f, g|] (4) yields [64, 7]
16
Apply-To-All

A functional form that takes a single
function as a parameter and yields a list of
values obtained by applying the given
function to each element of a list of
parameters
Form: 
For h (x)  x * x * x
(h, [3, 2, 4]) yields [27, 8, 64]
17
Lambda Calculus

Lambda notation is a way of denoting
functions
 Invented
by Alonzo Church in the 1930s.
 x.E[x], ‘the function of x that yields E[x]’

Example
 x.x
 Squaring
function?
18
Why Lambda?




In informal mathematics, when talking about a function,
one normally gives it a name, e.g.,
“define f(x) = E[x] … then … f …”
Similarly, most programming languages will only let you
define functions if you are prepared to give them a name.
This approach is rather inconsistent, as we are not
treating functions on a par with other mathematical
objects. E.g., we don’t say
“define x and y by x = 2 and y = 4 respectively. Then x*x
= y.”
Lambda notation helps to put functions on an equal
footing. This is important in functional programming.
19
Example --- Currying
Functions of more than one argument
x.y.x + y
Q: Signature?
Q: (x.y.x + y) 1 2
 Covention
x y.E[x,y] means x.y.E[x,y]

20
Exercise (5 Minutes In Pairs)

Evaluate the following  expressions.
 (x.x)
(x.x)
 (x.y.(y x)) 0 (x.x)
 (x.y.(x y)) ((x.x) (x.x)) (x.x)
21
Formal Lamda Calculus

Syntax of lambda expressions
<lambda-expr> -> <variable>
| <constant>
| <application>
| <abstraction>
<application> -> ( <lambda-expr> ) <lambda-expr>
<abstraction> ->  <variable> . <lambda-expr>

Examples
x, y, …
0, 1, false, true, …
(x.x) 0, (x.x) false, …
x.x, …
22
Exercise

Extend the lambda expression syntax to include unary
operators, binary operators, and if-then-else expression.
<lambda-expr> -> …
| <unary-opr> <lambda-expr>
| <lambda-expr> <binary-opr> <lambda-expr>
| if <lambda-expr> then <lambda-expr> else <lambda-expr>

Examples
-x, x + y, if x > 0 then 1 else -1, …
23
Exercise

Define a lambda abstraction of the following
function, f : R  R, defined as
f(x) =
0
x2sin(1/x2)
if x = 0
if x != 0
24
Local Definitions
<lambda-expr> -> …
| let <name> = <lambda-expr> in <lambda-expr>
Examples
let x = 10 in x
let incr = x.x+1 in incr 10
let f = x.if x == 0 then 1 else x * f (x – 1)
in f 3
25
Group Exercise

In lambda notation, define higher-order functions of
functional composition, construction, and apply-to-all.
For construction and apply-to-all, you may use Haskell
lists operators and functions, such as:





[] – empty list
: – concatenation of element and list, e.g., 1 : [2]  [1, 2]
length – number of elements in a list, e.g., length [2, 3]  2
head – first element of a list, e.g., head [1, 2]  1
tail – list containing all elements except for the first, e.g.,
tail [1, 2, 3]  [2, 3], tail [1]  []
26
In Sum, Fundamentals of FPL


The objective of the design of a FPL is to mimic
mathematical functions to the greatest extent possible.
The basic process of computation is fundamentally
different in a FPL than in an imperative language.



In an imperative language, operations are done and the results
are stored in variables for later use
Management of variables is a constant concern and source of
complexity for imperative programming.
In an FPL, variables are not necessary, as is the case in
mathematics.
27
Fundamentals of FPL (Cont.)

In an FPL, the evaluation of a function
always produces the same result given the
same parameters.
 This
is called referential transparency.
28