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{{for|subroutine-oriented programming|Procedural programming}}{{short description|Programming paradigm}}{{Programming paradigms}}In computer science, functional programming is a programming paradigm—a style of building the structure and elements of computer programs—that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data. It is a declarative programming paradigm in that programming is done with expressions or declarationsWEB,weblink Declaration vs. expression style - HaskellWiki, instead of statements. Functional code is idempotent: a function's return value depends only on its arguments, so calling a function with the same value for an argument always produces the same result. This is in contrast to imperative programming where, in addition to a function's arguments, global program state can affect a function's resulting value. Eliminating side effects, that is, changes in state that do not depend on the function inputs, can make understanding a program easier, which is one of the key motivations for the development of functional programming.Functional programming has its origins in lambda calculus, a formal system developed in the 1930s to investigate computability, the Entscheidungsproblem, function definition, function application, and recursion. Many functional programming languages can be viewed as elaborations on the lambda calculus. Another well-known declarative programming paradigm, logic programming, is based on relations.In contrast, imperative programming changes state with statements in the source code, the simplest example being assignment. Imperative programming has subroutines, but these are not mathematical functions. They can have side effects that may change a program's state, allowing for functions without return values. Because of this, they lack referential transparency, that is, the same language expression can result in different values at different times depending on the state of the executing program.JOURNAL, Hudak, Paul, Paul Hudak, Conception, evolution, and application of functional programming languages, ACM Computing Surveys, 21, 3, 359–411, September 1989,weblink 10.1145/72551.72554, Functional programming languages have largely been emphasized in academia rather than industry settings. However, programming languages that support functional programming have been used in industry, including Common Lisp, Scheme, Clojure, Wolfram Language,WEB, Wolfram Language Guide: Functional Programming,weblink 2015, 2015-08-24, Racket, Erlang, OCaml, Haskell, and F#.CONFERENCE, Mansell, Howard, Quantitative Finance in F#,weblink 2008, CUFP 2008, 2009-08-29, CONFERENCE, Peake, Alex, The First Substantial Line of Business Application in F#,weblink 2009, CUFP 2009, 2009-08-29, JavaScript, one of the world's most widely distributed languages,WEB, The 15 most popular computer languages, according to the Facebook for programmers,weblink 2016, 2017-07-31, WEB, JavaScript is the World's Dominant Programming Language,weblink 2016, 2017-07-31, WEB, Concepts of Functional Programming in Javascript,weblink 2018, 2019-03-07, has the properties of a dynamically typed functional language, in addition to imperative and object-oriented paradigms. Functional programming is also key to some languages that have found success in specific domains, like R in statistics, J, K and Q in financial analysis, and XQuery/XSLT for XML. Domain-specific declarative languages like SQL and Lex/Yacc use some elements of functional programming, especially in not supporting mutable values.Programming in a functional style can be accomplished in languages that are not specifically designed for functional programming, such as with Perl,BOOK, Dominus, Mark J., Mark Jason Dominus, Higher-Order Perl, Morgan Kaufmann, 2005, 978-1-55860-701-9, Higher-Order Perl, PHP,BOOK, Holywell, Simon, Functional Programming in PHP, php[architect], 2014, 9781940111056, C++11, and Kotlin.WEB,weblink Functional programming - Kotlin Programming Language, Kotlin, 2019-05-01, An interesting case is that of Scala – it is frequently written in a functional style, but the presence of side effects and mutable state place it in a grey area between imperative and functional languages.

History

Lambda calculus provides a theoretical framework for describing functions and their evaluation. It is a mathematical abstraction rather than a programming language—but it forms the basis of almost all current functional programming languages. An equivalent theoretical formulation, combinatory logic, is commonly perceived as more abstract than lambda calculus and preceded it in invention. Combinatory logic and lambda calculus were both originally developed to achieve a clearer approach to the foundations of mathematics.BOOK, Haskell Brooks Curry, Robert Feys, Combinatory Logic,weblink 10 February 2013, 1958, North-Holland Publishing Company, An early functional-flavored language was Lisp, developed in the late 1950s for the IBM 700/7000 series scientific computers by John McCarthy while at Massachusetts Institute of Technology (MIT).JOURNAL, John, McCarthy, John McCarthy (computer scientist), History of Lisp, In ACM/SIGPLAN History of Programming Languages Conference, 217–223, June 1978,weblink 10.1145/800025.808387, Lisp first introduced many paradigmatic features of functional programming, though early Lisps were multi-paradigm languages, and incorporated support for numerous programming styles as new paradigms evolved. Later dialects, such as Scheme and Clojure, and offshoots such as Dylan and Julia, sought to simplify and rationalise Lisp around a cleanly functional core, while Common Lisp was designed to preserve and update the paradigmatic features of the numerous older dialects it replaced.BOOK, Guy L. Steele, Richard P. Gabriel, The Evolution of Lisp, In ACM/SIGPLAN Second History of Programming Languages, 233–330, February 1996,weblink 10.1145/234286.1057818, 978-0-201-89502-5, Information Processing Language (IPL), 1956, is sometimes cited as the first computer-based functional programming language.The memoir of Herbert A. Simon (1991), Models of My Life pp.189-190 {{ISBN|0-465-04640-1}} claims that he, Al Newell, and Cliff Shaw are "...commonly adjudged to be the parents of [the] artificial intelligence [field]," for writing Logic Theorist, a program that proved theorems from Principia Mathematica automatically. To accomplish this, they had to invent a language and a paradigm that, viewed retrospectively, embeds functional programming. It is an assembly-style language for manipulating lists of symbols. It does have a notion of generator, which amounts to a function that accepts a function as an argument, and, since it is an assembly-level language, code can be data, so IPL can be regarded as having higher-order functions. However, it relies heavily on mutating list structure and similar imperative features.Kenneth E. Iverson developed APL in the early 1960s, described in his 1962 book A Programming Language ({{ISBN|9780471430148}}). APL was the primary influence on John Backus's FP. In the early 1990s, Iverson and Roger Hui created J. In the mid-1990s, Arthur Whitney, who had previously worked with Iverson, created K, which is used commercially in financial industries along with its descendant Q.John Backus presented FP in his 1977 Turing Award lecture "Can Programming Be Liberated From the von Neumann Style? A Functional Style and its Algebra of Programs".WEB,weblink Can Programming Be Liberated from the von Neumann Style? A Functional Style and Its Algebra of Programs, He defines functional programs as being built up in a hierarchical way by means of "combining forms" that allow an "algebra of programs"; in modern language, this means that functional programs follow the principle of compositionality.{{Citation needed|reason=I dont completely agree with this interpretation of John Backus definition of functional programs, which I feel is widely misunderstood. As he is very sadly no longer alive we can't ask him, but a reference for this interpretation, especially if it includes a justification, would be very beneficial.|date=February 2017}} Backus's paper popularized research into functional programming, though it emphasized function-level programming rather than the lambda-calculus style now associated with functional programming.The 1973 language ML was created by Robin Milner at the University of Edinburgh, and David Turner developed the language SASL at the University of St Andrews. Also in Edinburgh in the 1970s, Burstall and Darlington developed the functional language NPL.R.M. Burstall. Design considerations for a functional programming language. Invited paper, Proc. Infotech State of the Art Conf. "The Software Revolution", Copenhagen, 45–57 (1977) NPL was based on Kleene Recursion Equations and was first introduced in their work on program transformation.R.M. Burstall and J. Darlington. A transformation system for developing recursive programs. Journal of the Association for Computing Machinery 24(1):44–67 (1977) Burstall, MacQueen and Sannella then incorporated the polymorphic type checking from ML to produce the language Hope.R.M. Burstall, D.B. MacQueen and D.T. Sannella. HOPE: an experimental applicative language. Proc. 1980 LISP Conference, Stanford, 136–143 (1980). ML eventually developed into several dialects, the most common of which are now OCaml and Standard ML.Meanwhile, the development of Scheme, a simple lexically scoped and (impurely) functional dialect of Lisp, as described in the influential Lambda Papers and the classic 1985 textbook Structure and Interpretation of Computer Programs, brought awareness of the power of functional programming to the wider programming-languages community.In the 1980s, Per Martin-Löf developed intuitionistic type theory (also called constructive type theory), which associated functional programs with constructive proofs expressed as dependent types. This led to new approaches to interactive theorem proving and has influenced the development of subsequent functional programming languages.{{citation needed|date=July 2018}} The lazy functional language, Miranda, developed by David Turner, initially appeared in 1985 and had a strong influence on Haskell. With Miranda being proprietary, Haskell began with a consensus in 1987 to form an open standard for functional programming research; implementation releases have been ongoing since 1990.More recently it has found use in niches such as parametric CAD courtesy of the OpenSCAD language built on the CSG geometry framework, although its inability to reassign values has led to much confusion among users who are often unfamiliar with Functional programming as a concept.WEB,weblink OpenSCAD, Make discovering assign() easier!, Functional programming continues to be used in commercial settings.WEB,weblink Developers love trendy new languages but earn more with functional programming, Peter Bright, March 13, 2018, Ars Technica, WEB,weblink The stealthy rise of functional programming, John Leonard, January 24, 2017, Computing, MAGAZINE,weblink Is functional programming better for your startup?, Leo Cheung, May 9, 2017, InfoWorld,

Concepts

A number of concepts and paradigms are specific to functional programming, and generally foreign to imperative programming (including object-oriented programming). However, programming languages often cater to several programming paradigms, so programmers using "mostly imperative" languages may have utilized some of these concepts.WEB,weblinkweblink" title="web.archive.org/web/20060827094123weblink">weblink 2006-08-27, Functional Programming Comes of Age, Dick Pountain, BYTE.com (August 1994), August 31, 2006,

First-class and higher-order functions

Higher-order functions are functions that can either take other functions as arguments or return them as results. In calculus, an example of a higher-order function is the differential operator d/dx, which returns the derivative of a function f.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 that applies a function to its arguments one at a time, with each application returning a new function that accepts the next argument. This lets a programmer succinctly express, for example, the successor function as the addition operator partially applied to the natural number one.

Pure functions

Pure functions (or expressions) have no side effects (memory or I/O). 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 arguments that cause no side-effects, the result is constant with respect to that argument list (sometimes called referential transparency), i.e., calling the pure function again with the same arguments returns the same result. (This can enable caching optimizations such as memoization.)
  • If there is no data dependency between two pure expressions, 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 also lets functions be designated pure.JOURNAL, ISO/IEC JTC 1/SC 22/WG5/N2137, International Organization for Standardization, July 6, 2017,weblink C++11 added constexpr keyword with similar semantics.

Recursion

Iteration (looping) in functional languages is usually accomplished via recursion. Recursive functions invoke themselves, letting an operation be repeated until it reaches the base case. Although some recursion requires maintaining a stack, 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 abstracted away using higher-order functions, with catamorphisms and anamorphisms (or "folds" and "unfolds") being the most obvious examples. Such recursion schemes 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.JOURNAL, Turner, D.A., David Turner (computer scientist), Total Functional Programming, Journal of Universal Computer Science, 10, 2004-07-28, 751–768,weblink 10.3217/jucs-010-07-0751, 7,

Strict versus non-strict evaluation

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 fails. For example, the expression:
print length([2+1, 3*2, 1/0, 5-4])
fails under strict evaluation because of the division by zero in the third element of the list. Under lazy evaluation, the length function returns the value 4 (i.e., the number of items in the list), since evaluating it does not attempt to evaluate the terms making up the list. In brief, strict evaluation always fully evaluates function arguments before invoking the function. Lazy evaluation does not evaluate function arguments unless their values are required to evaluate the function call itself.The usual implementation strategy for lazy evaluation in functional languages is graph reduction.The Implementation of Functional Programming Languages. Simon Peyton Jones, published by Prentice Hall, 1987 Lazy evaluation is used by default in several pure functional languages, including Miranda, Clean, and Haskell.{{Harvnb|Hughes|1984}} argues for lazy evaluation as a mechanism for improving program modularity through separation of concerns, by easing independent implementation of producers and consumers of data streams.WEB,weblink Why Functional Programming Matters, John Hughes (computer scientist), John, Hughes, 1984, harv, 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.JOURNAL, John Launchbury, John_Launchbury, A Natural Semantics for Lazy Evaluation, 1993, 10.1.1.35.2016, Harper 2009 proposes including both strict and lazy evaluation in the same language, using the language's type system to distinguish them.BOOK,weblinkweblink" title="web.archive.org/web/20160407095249weblink">weblink 2016-04-07, Practical Foundations for Programming Languages, Robert W. Harper, Robert_Harper_(computer_scientist), 2009,

Type systems

Especially since the development of Hindley–Milner type inference in the 1970s, functional programming languages have tended to use typed lambda calculus, rejecting all invalid programs at compilation time and risking false positive errors, as opposed to the untyped lambda calculus, that accepts all valid programs at compilation time and risks false negative errors, used in Lisp and its variants (such as Scheme), though they reject all invalid programs at runtime, when the information is enough to not reject valid programs. The use of algebraic datatypes makes manipulation of complex data structures convenient; the presence of strong compile-time type checking makes programs more reliable in absence of other reliability techniques like test-driven development, while type inference frees the programmer from the need to manually declare types to the compiler in most cases.Some research-oriented functional languages such as Coq, Agda, Cayenne, and Epigram are based on intuitionistic type theory, which lets types 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.JOURNAL, Huet, Gérard P., 1973, The Undecidability of Unification in Third Order Logic, Information and Control, 10.1016/s0019-9958(73)90301-x, 22, 3, 257–267, THESIS, Ph.D., Huet, Gérard, Sep 1976, Resolution d'Equations dans des Langages d'Ordre 1,2,...ω, French, Universite de Paris VII, BOOK, Huet, Gérard, 2002, Carreño, V., Muñoz, C., Tahar, S., Higher Order Unification 30 years later, Proceedings, 15th International Conference TPHOL, 2410, 3–12, Springer, LNCS,weblink JOURNAL, J. B., Wells, Typability and type checking in the second-order lambda-calculus are equivalent and undecidable, 10.1.1.31.3590, Tech. Rep. 93-011, 1993, 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.WEB,weblink The Compcert verified compiler, Leroy, Xavier, 17 September 2018, 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.JOURNAL,weblink Simple unification-based type inference for GADTs, Simon Peyton Jones, Dimitrios Vytiniotis, Stephanie Weirich, Geoffrey Washburn, Icfp 2006, 50–61, April 2006, GADT's are available in the Glasgow Haskell Compiler, in OCaml (since version 4.00) and in Scala (as "case classes"), and have been proposed as additions to other languages including Java and C#.BOOK, Generalized Algebraic Data Types and Object-Oriented Programming, Andrew Kennedy, Claudio Russo, OOPSLA, October 2005, San Diego, California,weblinkweblink" title="web.archive.org/web/20061229164852weblink">weblink 2006-12-29, 9781595930316, source of citation

Referential transparency

Functional programs do not have assignment statements, that is, the value of a variable in a functional program never changes once defined. This eliminates any chances of side effects because any variable can be replaced with its actual value at any point of execution. So, functional programs are referentially transparent.WEB, Huges, John, Why Functional Programming Matters,weblink chalmers.se/cse, Chalmers Tekniska H¨ogskola, Consider C assignment statement x = x * 10, this changes the value assigned to the variable x. Let us say that the initial value of x was 1, then two consecutive evaluations of the variable x yields 10 and 100 respectively. Clearly, replacing x = x * 10 with either 10 or 100 gives a program with different meaning, and so the expression is not referentially transparent. In fact, assignment statements are never referentially transparent.Now, consider another function such as int plusone(int x) {return x+1;} is transparent, as it does not implicitly change the input x and thus has no such side effects.Functional programs exclusively use this type of function and are therefore referentially transparent.

Functional programming in non-functional languages

It is possible to use a functional style of programming in languages that are not traditionally considered functional languages.JOURNAL, Hartel, Pieter, Henk Muller, Hugh Glaser, The Functional C experience, Journal of Functional Programming, 14, 2, 129–135, March 2004,weblink 10.1017/S0956796803004817, ; WEB, Functional programming in Python, Part 3,weblinkweblink" title="web.archive.org/web/20071016124848weblink">weblink 2007-10-16, David Mertz, 2006-09-17, IBM developerWorks, (weblink" title="web.archive.org/web/20071016124848weblink">Part 1, weblink" title="web.archive.org/web/20071016124848weblink">Part 2) For example, both DWEB,weblink Functions â€” D Programming Language 2.0, Digital Mars, 30 December 2012, and Fortran 95 explicitly support pure functions.JavaScript, LuaWEB,weblink Lua Unofficial FAQ (uFAQ), and Python had first class functions from their inception.WEB,weblink Brendan, Eich, Popularity, 3 April 2008, Python had support for "lambda", "map", "reduce", and "filter" in 1994, as well as closures in Python 2.2,WEB,weblink Origins of Python's "Functional" Features, van Rossum, Guido, Guido van Rossum, 2009-04-21, 2012-09-27, though Python 3 relegated "reduce" to the functools standard library module.WEB,weblink functools — Higher order functions and operations on callable objects, Python Software Foundation, 2011-07-31, 2011-07-31, First-class functions have been introduced into other mainstream languages such as PHP 5.3, Visual Basic 9, C# 3.0, C++11, and Kotlin.{{citation needed|date=April 2015}}In PHP, anonymous classes, closures and lambdas are fully supported. Libraries and language extensions for immutable data structures are being developed to aid programming in the functional style.In Java, anonymous classes can sometimes be used to simulate closures;BOOK, Skarsaune, Martin, The SICS Java Port Project Automatic Translation of a Large Object Oriented System from Smalltalk to Java, 2008, however, anonymous classes are not always proper replacements to closures because they have more limited capabilities.WEB, Gosling, James, Closures,weblink James Gosling: on the Java Road, Oracle, 11 May 2013,weblink 2013-04-14, yes, Java 8 supports lambda expressions as a replacement for some anonymous classes.WEB,weblink Java SE 8 Lambda Quick Start, Michael, Williams, 8 April 2013, However, the presence of checked exceptions in Java can make functional programming inconvenient, because it can be necessary to catch checked exceptions and then rethrow them—a problem that does not occur in other JVM languages that do not have checked exceptions, such as Scala.{{Citation needed|date=March 2014}}In C#, anonymous classes are not necessary, because closures and lambdas are fully supported. Libraries and language extensions for immutable data structures are being developed to aid programming in the functional style in C#.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.Similarly, the idea of immutable data from functional programming is often included in imperative programming languages,BOOK, Effective Java, Second, Joshua, Bloch, Item 15: Minimize Mutability, 978-0321356680, 2008, Addison-Wesley, for example the tuple in Python, which is an immutable array.

Data structures

Purely functional data structures are often represented in a different way than their imperative counterparts.Purely functional data structures by Chris Okasaki, Cambridge University Press, 1998, {{ISBN|0-521-66350-4}} For example, the array with constant access and update times is a basic component of most imperative languages, and many imperative data-structures, such as the hash table and binary heap, are based on arrays. Arrays can be replaced by maps or random access lists, which admit purely functional implementation, but have logarithmic access and update times. Purely functional data structures have persistence, a property of keeping previous versions of the data structure unmodified. In Clojure, persistent data structures are used as functional alternatives to their imperative counterparts. Persistent vectors, for example, use trees for partial updating. Calling the insert method will result in some but not all nodes being created.WEB,weblink polymatheia - Understanding Clojure's Persistent Vector, pt. 1, (hyPiRion), Jean Niklas L'orange, www.hypirion.com, 2018-11-13,

Comparison to imperative programming

Functional programming is very different from imperative programming. The most significant differences stem from the fact that functional programming avoids side effects, which are used in imperative programming to implement state and I/O. Pure functional programming completely prevents side-effects and provides referential transparency.Higher-order functions are rarely used in older imperative programming. A traditional imperative program might use a loop to traverse and modify a list. A functional program, on the other hand, would probably use a higher-order “map” function that takes a function and a list, generating and returning a new list by applying the function to each list item.

Simulating state

There are tasks (for example, maintaining a bank account balance) that often seem most naturally implemented with state. Pure functional programming performs these tasks, and I/O tasks such as accepting user input and printing to the screen, in a different way.The pure functional programming language Haskell implements them using monads, derived from category theory. Monads offer a way to abstract certain types of computational patterns, including (but not limited to) modeling of computations with mutable state (and other side effects such as I/O) in an imperative manner without losing purity. While existing monads may be easy to apply in a program, given appropriate templates and examples, many students find them difficult to understand conceptually, e.g., when asked to define new monads (which is sometimes needed for certain types of libraries).WEB, Newbern, J., All About Monads: A comprehensive guide to the theory and practice of monadic programming in Haskell,weblink 2008-02-14, Functional languages also simulate states by passing around immutable states. This can be done by making a function accept the state as one of its parameters, and return a new state together with the result, leaving the old state unchanged.WEB,weblink Thirteen ways of looking at a turtle {{!, F# for fun and profit|website=fsharpforfunandprofit.com|language=en|access-date=2018-11-13}}Impure functional languages usually include a more direct method of managing mutable state. Clojure, for example, uses managed references that can be updated by applying pure functions to the current state. This kind of approach enables mutability while still promoting the use of pure functions as the preferred way to express computations.{{citation needed|date=July 2018}}Alternative methods such as Hoare logic and uniqueness have been developed to track side effects in programs. Some modern research languages use effect systems to make the presence of side effects explicit.{{citation needed|date=July 2018}}

Efficiency issues

Functional programming languages are typically less efficient in their use of CPU and memory than imperative languages such as C and Pascal.BOOK, Larry C. Paulson, ML for the Working Programmer,weblink 10 February 2013, 28 June 1996, Cambridge University Press, 978-0-521-56543-1, This is related to the fact that some mutable data structures like arrays have a very straightforward implementation using present hardware (which is a highly evolved Turing machine). Flat arrays may be accessed very efficiently with deeply pipelined CPUs, prefetched efficiently through caches (with no complex pointer chasing), or handled with SIMD instructions. It is also not easy to create their equally efficient general-purpose immutable counterparts. For purely functional languages, the worst-case slowdown is logarithmic in the number of memory cells used, because mutable memory can be represented by a purely functional data structure with logarithmic access time (such as a balanced tree). However, such slowdowns are not universal. For programs that perform intensive numerical computations, functional languages such as OCaml and Clean are only slightly slower than C according to The Computer Language Benchmarks Game.WEB,weblink Which programs are fastest? | Computer Language Benchmarks Game, benchmarksgame.alioth.debian.org, 2011-06-20, For programs that handle large matrices and multidimensional databases, array functional languages (such as J and K) were designed with speed optimizations.Immutability of data can in many cases lead to execution efficiency by allowing the compiler to make assumptions that are unsafe in an imperative language, thus increasing opportunities for inline expansion.JOURNAL, Immutability specification and its applications, Igor Pechtchanski, Vivek Sarkar, Concurrency and Computation: Practice and Experience, 17, 5–6, 639–662, 2005, 10.1002/cpe.853, Lazy evaluation may also speed up the program, even asymptotically, whereas it may slow it down at most by a constant factor (however, it may introduce memory leaks if used improperly). Launchbury 1993 discusses theoretical issues related to memory leaks from lazy evaluation, and O'Sullivan et al. 2008WEB,weblink Chapter 25. Profiling and optimization, Book.realworldhaskell.org, 2011-06-20, give some practical advice for analyzing and fixing them.However, the most general implementations of lazy evaluation making extensive use of dereferenced code and data perform poorly on modern processors with deep pipelines and multi-level caches (where a cache miss may cost hundreds of cycles) {{Citation needed|date=June 2014}}.

Coding styles

{{unreferenced section|date=July 2013}}Imperative programs have the environment and a sequence of steps manipulating the environment. Functional programs have an expression that is successively substituted until it reaches normal form. An example illustrates this with different solutions to the same programming goal (calculating Fibonacci numbers).

Java

Get Fibonacci number
public UnaryOperator fib(Integer acc, Integer next) {
return x -> {
return (x > 0) ? fib(acc + next, acc).apply(--x) : acc;
};
}
System.out.println(fib(0, 1).apply(5));

OCAML

Printing first 10 Fibonacci numberslet fib_list =
let rec fib_list_aux first second = function
| 0 -> []
| x -> first::fib_list_aux second (first+second) (x-1)
in
fib_list_aux 0 1
let () = List.iter (Printf.printf "%d ") (fib_list 10)

PHP

Printing first 10 Fibonacci numbers, using functionfunction fib(int $n) : int {
return ($n === 0 || $n === 1) ? $n : fib($n - 1) + fib($n - 2);
}for ($i = 0; $i
if n == 0 then [] else
[first] ++ fibonacci_aux (n - 1) second (first + second)
fibonacci = n-> fibonacci_aux n 0 1main = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional expression stylefibonacci = n-> if n == 0 then 0
else if n == 1 then 1
else fibonacci(n - 1) + fibonacci(n - 2)
main = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional expression style, tail recursivefibonacci_aux = n first second->
if n == 0 then first else
fibonacci_aux (n - 1) second (first + second)
fibonacci = n-> Fibonacci_aux n 0 1main = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional expression style with recursive listsfibonacci_aux = first second-> first : fibonacci_aux second (first + second)select = n zs-> if n==0 then head zs
else select (n - 1) (tail zs)
fibonacci = n-> select n (fibonacci_aux 0 1)main = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional expression style with primitives for recursive listsfibonacci_aux = first second-> first : fibonacci_aux second (first + second)fibonacci = n-> (fibonacci_aux 0 1) !! nmain = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional expression style with primitives for recursive lists, more conciselyfibonacci_aux = 0:1:zipWith (+) fibonacci_aux (tail fibonacci_aux)fibonacci = n-> fibonacci_aux !! nmain = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional declaration stylefibonacci 0 = 0fibonacci 1 = 1fibonacci n = fibonacci (n-1) + fibonacci (n-2)main = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional declaration style, tail recursivefibonacci_aux 0 first _ = firstfibonacci_aux n first second = fibonacci_aux (n - 1) second (first + second)fibonacci n = fibonacci_aux n 0 1main = putStrLn (show (fibonacci 10))Printing the 11th Fibonacci number, functional declaration style, using lazy infinite lists and primitivesfibs = 1 : 1 : zipWith (+) fibs (tail fibs)-- an infinite list of the fibonacci numbers-- fibs is defined in terms of fibsfibonacci = (fibs !!)main = putStrLn $ show $ fibonacci 11Printing the first 10 Fibonacci numbers, list comprehension (generator) stylefibs = [0, 1] ++ [(fibs !! x) + (fibs !! (x + 1)) | x UInt:D ) {*}multi fib ( 0 --> 0 ) { }multi fib ( 1 --> 1 ) { }multi fib ( n ) {
fib(n - 1) + fib(n - 2)
}for ^10 -> $n { say fib($n) }An alternative to this is to construct a lazy iterative sequence, which appears as an almost direct illustration of the sequence:my @fib = 0, 1, *+* ... *; # Each additional entry is the sum of the previous two
# and this sequence extends lazily indefinitely
say @fib[^10]; # Display the first 10 entries

Erlang

Erlang is a functional, concurrent, general-purpose programming language. A Fibonacci algorithm implemented in Erlang (Note: This is only for demonstrating the Erlang syntax. Use other algorithms for fast performanceWEB,weblink" title="web.archive.org/web/20131226033005weblink">weblink yes,weblink 26 December 2013, Fibonacci Sequence Recursion in Erlang, Thorp, Noah, 16 February 2008, Aquabu, ):-module(fib). % This is the file 'fib.erl', the module and the filename must match-export([fib/1]). % This exports the function 'fib' of arity 1fib(1) -> 1; % If 1, then return 1, otherwise (note the semicolon ; meaning 'else')fib(2) -> 1; % If 2, then return 1, otherwisefib(N) -> fib(N - 2) + fib(N - 1).

Elixir

Elixir is a functional, concurrent, general-purpose programming language that runs on the Erlang virtual machine (BEAM).The Fibonacci function can be written in Elixir as follows:defmodule Fibonacci do
def fib(0), do: 0
def fib(1), do: 1
def fib(n), do: fib(n-1) + fib(n-2)
endEnum.map(1..10, &Fibonacci.fib/1)

Lisp

The Fibonacci function can be written in Common Lisp as follows:(defun fib (n &optional (a 0) (b 1))
(if (= n 0)
a
(fib (- n 1) b (+ a b))))
or(defun fib (n)
(if (or (= n 0) (= n 1))
n
(+ (fib (- n 1)) (fib (- n 2)))))
The program can then be called as(fib 10)

Clojure

The Fibonacci function can be written in Clojure as follows:(defn fib
[n]
(loop [a 0 b 1 i n]
(if (zero? i)
a
(recur b (+ a b) (dec i)))))
The program can then be called as(fib 7)Explicitly using "lazy-seq", the infinite sequence of Fibonacci numbers can be defined recursively.
lazy infinite sequence
(def fibs (cons 0 (cons 1 (lazy-seq (map + fibs (rest fibs))))))
list of first 10 Fibonacci numbers taken from infinite sequence
(take 10 fibs)

Kotlin

The Fibonacci function can be written in Kotlin as follows:fun fib(x: Int): Int = if (x in 0..1) x else fib(x - 1) + fib(x - 2)The program can then be called asfib(7)

Swift

The Fibonacci function can be written in Swift as follows:func fib(_ x: Int) -> Int {
if x == 0 || x == 1 {
return x
} else {
return fib(x - 1) + fib(x - 2)
}
}The function can then be called asfib(7)

JavaScript

The Fibonacci function can be written in JavaScript as follows:const fib = (x) => (function sub_fib(a, b) { return x-- > 0 ? sub_fib(b, a+b) : a})(0,1)orconst fib = (x, a = 0, b = 1) => x > 0 ? fib(x - 1, b, a + b) : aor const fib = x => x x n { (n == 0 || n == 1) ? n : fib[n - 1] + fib[n - 2] }

Tcl

The Fibonacci function can be written in Tcl as a recursive function as follows:proc fibo {x} {
expr {$x 1
case _ => fibRec(n - 1) + fibRec(n - 2)
}Recursive style with tail call optimization, fastdef fibTailRec(n: Int): Int = {
@tailrec
def fib(i: Int, v: Int, vNext: Int): Int =
if(i == n) v
else fib(i + 1, vNext, v + vNext)
fib(0, 0, 1)
}Using Scala streamsval fibStream: Stream[Int] =
0 #:: 1 #:: (fibStream zip fibStream.tail).map(n => n._1 + n._2)

Use in industry

Functional programming has long been popular in academia, but with few industrial applications.BOOK, Martin, Odersky, Lex, Spoon, Bill, Venners, December 13, 2010, Programming in Scala: A Comprehensive Step-by-step Guide, Artima, 2nd, 883/852, 978-0-9815316-4-9,weblink {{rp|page 11}} However, recently several prominent functional programming languages have been used in commercial or industrial systems.JOURNAL, Ray, Baishakhi, Posnett, Daryl, Devanbu, Premkumar, Filkov, Vladimir, 2017-09-25, A large-scale study of programming languages and code quality in GitHub,weblink Communications of the ACM, en, 60, 10, 92, 10.1145/3126905, For example, the Erlang programming language, which was developed by the Swedish company Ericsson in the late 1980s, was originally used to implement fault-tolerant telecommunications systems. It has since become popular for building a range of applications at companies such as Nortel, Facebook, Électricité de France and WhatsApp.CONFERENCE, Piro, Christopher, Functional Programming at Facebook,weblink 2009, CUFP 2009, 2009-08-29, 1 million is so 2011 // WhatsApp blog, 2012-01-06: "the last important piece of our infrastracture is Erlang" The Scheme dialect of Lisp was used as the basis for several applications on early Apple Macintosh computers, and has more recently been applied to problems such as training simulation software and telescope control. OCaml, which was introduced in the mid-1990s, has seen commercial use in areas such as financial analysis, driver verification, industrial robot programming, and static analysis of embedded software. Haskell, though initially intended as a research language, has also been applied by a range of companies, in areas such as aerospace systems, hardware design, and web programming.Other functional programming languages that have seen use in industry include Scala,CONFERENCE, Momtahan, Lee, Scala at EDF Trading: Implementing a Domain-Specific Language for Derivative Pricing with Scala,weblink 2009, CUFP 2009, 2009-08-29, F#, (both being functional-OO hybrids with support for both purely functional and imperative programming) Wolfram Language, Lisp,WEB, Graham, Paul, Beating the Averages,weblink 2003, 2009-08-29, Standard MLCONFERENCE, Sims, Steve, Building a Startup with Standard ML,weblink 2006, CUFP 2006, 2009-08-29, CONFERENCE, Laurikari, Ville, Functional Programming in Communications Security.,weblink 2007, CUFP 2007, 2009-08-29, and Clojure.WEB,weblink Lorimer, R. J., Live Production Clojure Application Announced, 19 January 2009, InfoQ,

In education

Functional programming is being used as a method to teach problem solving, algebra and geometric concepts.{{Triangulation|196|Emmanuel Schanzer of Bootstrap}}It has also been used as a tool to teach classical mechanics in Structure and Interpretation of Classical Mechanics.

See also

References

Further reading

  • BOOK, Abelson, Hal, Hal Abelson, Sussman, Gerald Jay, Gerald Jay Sussman, Structure and Interpretation of Computer Programs,weblink 1985, MIT Press,
  • Cousineau, Guy and Michel Mauny. The Functional Approach to Programming. Cambridge, UK: Cambridge University Press, 1998.
  • Curry, Haskell Brooks and Feys, Robert and Craig, William. Combinatory Logic. Volume I. North-Holland Publishing Company, Amsterdam, 1958.
  • BOOK, Curry, Haskell B., J. Roger, Hindley, Jonathan P., Seldin, Haskell Curry, J. Roger Hindley, Jonathan P. Seldin, Combinatory Logic, Vol. II, 1972, North Holland, Amsterdam, 978-0-7204-2208-5,
  • Dominus, Mark Jason. Higher-Order Perl. Morgan Kaufmann. 2005.
  • BOOK, Felleisen, Matthias, Findler, Robert, Flatt, Matthew, Shriram, Krishnamurthi, How to Design Programs,weblink 2001, MIT Press,
  • Graham, Paul. ANSI Common LISP. Englewood Cliffs, New Jersey: Prentice Hall, 1996.
  • MacLennan, Bruce J. Functional Programming: Practice and Theory. Addison-Wesley, 1990.
  • BOOK, O'Sullivan, Brian, Stewart, Don, Goerzen, John, Real World Haskell,weblink 2008, O'Reilly,
  • Pratt, Terrence, W. and Marvin V. Zelkowitz. Programming Languages: Design and Implementation. 3rd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1996.
  • Salus, Peter H. Functional and Logic Programming Languages. Vol. 4 of Handbook of Programming Languages. Indianapolis, Indiana: Macmillan Technical Publishing, 1998.
  • Thompson, Simon. Haskell: The Craft of Functional Programming. Harlow, England: Addison-Wesley Longman Limited, 1996.

External links

{{Spoken Wikipedia|En-Functional_programming.ogg|2011-08-25}}
  • WEB, Ford, Neal, Functional thinking: Why functional programming is on the rise, 2013-02-24, 2012-01-29,weblink
,
  • WEB, Akhmechet, Slava, defmacro – Functional Programming For The Rest of Us, 2013-02-24, 2006-06-19,weblink
, An introduction {{Programming language}}{{Authority control}}

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