Saturday, 26 March 2011


A number of concepts and paradigms are specific to functional programming, and generally foreign to imperative programming (including object oriented programming). However, programming languages are often hybrids of several programming paradigms so programmers using "mostly imperative" languages may have utilized some of these concepts.[24]
First-class and higher-order functions
Main articles: first-class function and higher-order function

Higher-order functions are functions that can either take other functions as arguments or return them as results (the differential operator d / dx that produces the derivative of a function f is an example of this in calculus).

Higher-order functions are closely related to first-class functions, in that higher-order functions and first-class functions both allow functions as arguments and results of other functions. The distinction between the two is subtle: "higher-order" describes a mathematical concept of functions that operate on other functions, while "first-class" is a computer science term that describes programming language entities that have no restriction on their use (thus first-class functions can appear anywhere in the program that other first-class entities like numbers can, including as arguments to other functions and as their return values).

Higher-order functions enable partial application or currying, a technique in which a function is applied to its arguments one at a time, with each application returning a new function that accepts the next argument. This allows one to succinctly express, for example, the successor function as the addition operator partially applied to the natural number one.
Pure functions

Purely functional functions (or expressions) have no memory or I/O side effects. This means that pure functions have several useful properties, many of which can be used to optimize the code:

    * If the result of a pure expression is not used, it can be removed without affecting other expressions.
    * If a pure function is called with parameters that cause no side-effects, the result is constant with respect to that parameter list (sometimes called referential transparency), i.e. if the pure function is again called with the same parameters, the same result will be returned (this can enable caching optimizations such as memoization).
    * If there is no data dependency between two pure expressions, then their order can be reversed, or they can be performed in parallel and they cannot interfere with one another (in other terms, the evaluation of any pure expression is thread-safe).
    * If the entire language does not allow side-effects, then any evaluation strategy can be used; this gives the compiler freedom to reorder or combine the evaluation of expressions in a program (for example, using deforestation).

While most compilers for imperative programming languages detect pure functions, and perform common-subexpression elimination for pure function calls, they cannot always do this for pre-compiled libraries, which generally do not expose this information, thus preventing optimizations that involve those external functions. Some compilers, such as gcc, add extra keywords for a programmer to explicitly mark external functions as pure, to enable such optimizations. Fortran 95 allows functions to be designated "pure".
Main article: Recursion (computer science)

Iteration (looping) in functional languages is usually accomplished via recursion. Recursive functions invoke themselves, allowing an operation to be performed over and over. Recursion may require maintaining a stack, but tail recursion can be recognized and optimized by a compiler into the same code used to implement iteration in imperative languages. The Scheme language standard requires implementations to recognize and optimize tail recursion. Tail recursion optimization can be implemented by transforming the program into continuation passing style during compiling, among other approaches.

Common patterns of recursion can be factored out using higher order functions, with catamorphisms and anamorphisms (or "folds" and "unfolds") being the most obvious examples. Such higher order functions play a role analogous to built-in control structures such as loops in imperative languages.

Most general purpose functional programming languages allow unrestricted recursion and are Turing complete, which makes the halting problem undecidable, can cause unsoundness of equational reasoning, and generally requires the introduction of inconsistency into the logic expressed by the language's type system. Some special purpose languages such as Coq allow only well-founded recursion and are strongly normalizing (nonterminating computations can be expressed only with infinite streams of values called codata). As a consequence, these languages fail to be Turing complete and expressing certain functions in them is impossible, but they can still express a wide class of interesting computations while avoiding the problems introduced by unrestricted recursion. Functional programming limited to well-founded recursion with a few other constraints is called total functional programming. See Turner 2004 for more discussion.[25]
Strict versus non-strict evaluation
Main article: Evaluation strategy

Functional languages can be categorized by whether they use strict (eager) or non-strict (lazy) evaluation, concepts that refer to how function arguments are processed when an expression is being evaluated. The technical difference is in the denotational semantics of expressions containing failing or divergent computations. Under strict evaluation, the evaluation of any term containing a failing subterm will itself fail. For example, the expression

 print length([2+1, 3*2, 1/0, 5-4])

will fail under strict evaluation because of the division by zero in the third element of the list. Under nonstrict evaluation, the length function will return the value 4, since evaluating it will not attempt to evaluate the terms making up the list. In brief, strict evaluation always fully evaluates function arguments before invoking the function. Non-strict evaluation does not evaluate function arguments unless their values are required to evaluate the function call itself.

The usual implementation strategy for non-strict evaluation in functional languages is graph reduction.[26] Non-strict evaluation is used by default in several pure functional languages, including Miranda, Clean and Haskell.

Hughes 1984 argues for non-strict evaluation as a mechanism for improving program modularity through separation of concerns, by easing independent implementation of producers and consumers of data streams.[27] Launchbury 1993 describes some difficulties that lazy evaluation introduces, particularly in analyzing a program's storage requirements, and proposes an operational semantics to aid in such analysis.[28] Harper 2009 proposes including both strict and nonstrict evaluation in the same language, using the language's type system to distinguish them.[29]
Type systems, polymorphism, algebraic datatypes and pattern matching

Especially since the development of Hindley–Milner type inference in the 1970s, functional programming languages have tended to use typed lambda calculus, as opposed to the untyped lambda calculus used in Lisp and its variants (such as Scheme). The use of algebraic datatypes and pattern matching makes manipulation of complex data structures more convenient and expressive; the presence of strong compile-time type checking makes programs more reliable, while type inference frees the programmer from the need to manually declare types to the compiler.

Some research-oriented functional languages such as Coq, Agda, Cayenne, and Epigram are based on intuitionistic type theory, which allows types to depend on terms. Such types are called dependent types. These type systems do not have decidable type inference and are difficult to understand and program with. But dependent types can express arbitrary propositions in predicate logic. Through the Curry–Howard isomorphism, then, well-typed programs in these languages become a means of writing formal mathematical proofs from which a compiler can generate certified code. While these languages are mainly of interest in academic research (including in formalized mathematics), they have begun to be used in engineering as well. Compcert is a compiler for a subset of the C programming language that is written in Coq and formally verified.[30]

A limited form of dependent types called generalized algebraic data types (GADT's) can be implemented in a way that provides some of the benefits of dependently-typed programming while avoiding most of its inconvenience.[31] GADT's are available in the Glasgow Haskell Compiler and in Scala (as "case classes"), and have been proposed as additions to other languages including Java and C#.[32]
Functional programming in non-functional languages

It is possible to employ a functional style of programming in languages that are not traditionally considered functional languages.[33] Amongst imperative programming languages, the D programming language's solid support for functional programming stands out. For example, D has a pure modifier to enforce functional purity. Only Fortran 95 has something similar.[34]

First class functions have slowly been added to mainstream languages. For example, in early 1994, support for lambda, filter, map, and reduce was added to Python. Then during the development of Python 3000, Guido van Rossum called for the removal of these features. So far only the reduce function has been removed.[35] First class functions were also introduced in PHP 5.3, Visual Basic 9, C# 3.0, and C++0x.

The Language Integrated Query (LINQ) feature, with its many incarnations, is an obvious and powerful use of functional programming in .NET.

In Java, anonymous classes can sometimes be used to simulate closures;[citation needed] however, anonymous classes are not always proper replacements to closures because they have more limited capabilities.

Many object-oriented design patterns are expressible in functional programming terms: for example, the strategy pattern simply dictates use of a higher-order function, and the visitor pattern roughly corresponds to a catamorphism, or fold.

The benefits of immutable data can be seen even in imperative programs, so programmers often strive to make some data immutable even in imperative programs

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