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To all the people who know lambda calculus: What benefit has it bought you, regarding programming? Would you recommend that people learn it?
The benefit of lambda calculus is that it's an extremely simple model of computation that is equivalent to a Turing machine. But while a Turing machine is more like assembly language, lambda calculus is more a like a high-level language. And if you learn Church encodings that will help you learn the programming technique called continuation-passing style, which is quite useful for implementing backtracking search and other neat tricks.
The main use of lambda calculus in practice is that it is a great laboratory tool for studying new programming-language ideas. If you have an idea for a new language feature, you can add the new feature to the lambda calculus and you get something that is expressive enough to program while being simple enough to study very thoroughly. This use is really more for language designers and theorists than for programmers.
Lambda calculus is also just very cool in its own right: just like knowing assembly language, it will deepen your understanding of computation. It's especially fun to program a universal turing machine in the lambda calculus. But this is foundational mathematics, not practical programming.
If you want to program in any functional programming language, it's essential. I mean, how useful is it to know about Turing machines? Well, if you write C, the language paradigm is quite close to Turing machines -- you have an instruction pointer and a current instruction, and the machine takes some action in the current state, and then ambles along to the next instruction.
In a functional language, you simply can't think like that -- that's not the language paradigm. You have to think back to lambda calculus, and how terms are evaluated there. It will be much harder for you to be effective in a functional language if you don't know lambda calculus.
To be honest, learning lambda calculus before functional programming has made me realize that the two are as unrelated as C is to any imperative programming.
Lambda calculus is a functional programming language, an esoteric one, a Turing tarpit if you like; accidentally it's also the first.
The majority of functional programming languages at all do not require you to 'learn' lambda calculus, whatever that would mean, lambda calculus is insanely minimal, you can 'learn' its axioms in an under an hour. To know the results from it, like the fixedpoint theorem, the Church-Rosser Theorem et cetera is just irrelevant to functional programming.
Also, lambda-abstractions are often held to be 'functions', I disagree with that, they are algorithms, not functions, a minor difference, most 'functional languages' treat their functions more in the way classical mathematics does.
However, to for instance effectively use Haskell you do need to understand certain type systems, that's irrespective of lambda calculus, the System F type system can be applied to all 'functions' and requires no lambda abstractions at all. Commonly in maths we say f : R^2 -> R : f (x) = x^2. We could've said: f (x) = x^2 :: R -> R -> R. In fact, Haskell comes pretty close to this notation.
Lambda calculus is a theoretical formalism, Haskell's functions are really no more 'lambda abstractions' than f : f(x) = x^2 really, what makes lambda abstractions interesting is that it enables us to define what are normally seen as 'constants' as 'functions', no functional language does that because of the huge computational overhead. Haskell and alike is just a restricted form of System F's type system applied to functions as used in everyday classical maths. Functions in Haskell are certainly not the anonymous formally symbolic reduction-applicants as they are in lambda-calculus. Most functional programming languages are not symbolic reduction-based re-writing systems. Lisps are to some degree but that's a paradigm on its own and its 'lambda keyword' really doesn't satisfy calling it lambda calculus.
I think the use of lambda calculus with respect to programming in practice is that it is a quite minimal system that captures the essence of abstraction (or "anonymous functions" or closures, if you will). Other than that I don't think it is generally essential except when you need to implement abstraction yourself (as Tetha (114646) mentioned).
I also completely disagree with Denis Bueno (114701) who says that it is essential for functional programming. It is perfectly well possible to define, use or understand a functional language without any lambda calculus at all. In order to understand the evaluation of terms in functional languages (which, in my opinion, somewhat contradicts the use of a functional language) you will most likely be better of learning about term rewrite systems.
I agree with those that say it is theoretically possible to learn functional programming without learning the lambda calculus—but what's the advantage of not learning the lambda calculus? It's not as if it takes a big investment of time.
Most likely, it will help you understand functional programming better. But even if it doesn't, it's still a cool thing worth learning. The Y-combinator is a thing of beauty.
If you only want to be a technician and write programs to do things, then you don't really need to know lambda-calculus, finite-state machines, pushdown automata, regular expressions, context-free grammar, discrete mathematics, etc.
But if you have curiosity about the deeper mysteries underlying this stuff, you can start to wonder how these questions might be answered. The concepts are beautiful and will expand your imagination. I also think they, incidentally, make one a better practicioner.
What got me hooked was Minsky's book Computation: Finite and Infinite Machines.
The lambda calculus is a computational model, just like the turing machine. Thus, it is useful if you need to implement a certain evaluator for a language based on this model, however, in practice, you just need the basic idea (uh. place argument semantically correct in the body of a function?) and that's about it.
One posible way to learn lambda calculus is
http://en.wikipedia.org/wiki/Lambda_Calculus
Or, if you want more, here is my blog dedicated to lambda calculus and stuff like that
http://weblogs.manas.com.ar/lziliani/
As every abstraction of computations, with lambda calculus you can model stuff used in most programming languages, like subtyping. For more about this, one of the best books with practical uses of lambda calculus in this sense is
http://www.amazon.com/Types-Programming-Languages-Benjamin-Pierce/dp/0262162091/ref=sr_1_1?ie=UTF8&s=books&qid=1222088714&sr=8-1
I found that the Lambda calculus was useful for understand how functional programming worked on a deeper level. Especially how to implement functional languages.
It has made it easier for me to understand advanced concepts like type-systems and evaluations strategies (e.g. call by name versus call by value).
I don't think one needs to know anything about the Lambda calculus to use basic functional programming techniques. However understanding the lambda calculus makes it easier to learn advanced programming theory.
I'd also like to mention that if you're doing anything in the area of NLP, lambda calculus is at the foundation of a massive body of work in compositional semantics.
The benefits for me is a more compact synergistic programming. Stuff tends to flow horizontally more than vertically. Plus it is very useful for prototyping simple algorithms. Don't know if I am using it to its full potential but I find it very useful.
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I would like to learn a functional language in order to broaden my horizon. I have knowledge of Python and C/C++ and I want a language to be easy to learn from someone who comes from the imperative domain of languages. I don't care if the language is powerful enough. I just want a language in order to learn the basic of functional programming and then I will try for a more difficult (and powerful language).
Thanks
I recommend pure-lang for these pedagogical ends. It's also plenty powerful. If you want something more popular / with more community support, then I'd recommend Scheme or OCaml, depending on whether you'd rather deal with unfamiliar syntax (go with Scheme) or deal with unfamiliar typing (go with OCaml) first. SML and F# are only slightly different from OCaml. Others have or will mention Clojure, Scala, and Haskell.
Clojure is a variant of Scheme, with its own idiosyncracies (e.g. no tail-call optimization), so using it would be a way of starting with Scheme. I'd expect you'd have an easier time with a less idiosyncratic Scheme implementation though. Racket is what's often used for teaching. Scala looks to be fundamentally similar to OCaml, but this is based on only casual familiarity.
Unlike Haskell, the other languages mentioned all have two advantages: (1) evaluation-order is eager by default, though you can get lazy evaluation by specifically requesting it. In Haskell's the reverse. (2) Mutation is available, though much of the libraries and code you'll see doesn't use it. I actually think it's pedagogically better to learn functional programming while at the same time having an eye on how it interacts with side-effects, and working your way to monadic-style composition somewhat down the road. So I think this is an advantage. Some will tell you that it's better to be thrown into Haskell's more-quarantined handling of mutaton first, though.
Robert Harper at CMU has some nice blog posts on teaching functional programming. As I understand, he also prefers languages like OCaml for teaching.
Among the three classes of languages I recommended (Pure, Scheme and friends, OCaml and friends), the first two have dynamic typing. The first and third have explicit reference cells (as though in Python, you restricted yourself to never reassiging a variable but you could still change what's stored at a list index). Scheme has implicit reference cells: variables themselves look mutable, as in C and Python, and the reference cell handling is done under the covers. In languages like that, you often have some form of explicit reference cell available too (as in the example I just gave in Python, or using mutable pairs/lists in Racket...in other Schemes, including the Scheme standard, those are the default pairs/lists).
One virtue Haskell does have is some textbooks are appearing for it. (I mean this sincerely, not snarkily.) What books/resources to use is another controversial issue with many wars/closed questions. SICP as others have recommended has many fans and also some critics. There seem to me to be many good choices. I won't venture further into those debates.
At first, read Structure and Implementation of Computer Programs. I recommend Lisp (for, example, it's dialect Scheme) as first functional programming language.
Another option is Clojure, which I'm given to understand is more "purely" functional than Scheme/Racket (don't ask me about the details here) and possibly similar enough to let you use it in conjunction with SICP (Structure and Interpretation of Computer Programs, a highly recommended book also suggested by another answer).
I would like to learn a functional language in order to broaden my horizon. I have knowledge of Python and C/C++ and I want a language to be easy to learn from someone who comes from the imperative domain of languages. I don't care if the language is powerful enough. I just want a language in order to learn the basic of functional programming and then I will try for a more difficult (and powerful language).
Great question!
I had done BASIC, Pascal, assembler, C and C++ before I started doing functional programming in the late 1990s. Then I started using two functional languages at about the same time, Mathematica and OCaml, and was using them exclusively within a few years. In particular, OCaml let me write imperative code which looked like the code I had been writing before. I found that valuable as a learner because it let me compare the different approaches which made the advantages of ML obvious.
However, as others have mentioned, the core benefit of Mathematica and OCaml is pattern matching and that is not technically related to functional programming. I have subsequently looked at many other functional languages but I have no desire to go back to a language that lacks pattern matching.
This question is probably off-topic because it is going to result in endless language wars, but here's a general bit of advice:
There are a class of functional programming languages which are sometimes called "mostly functional", in that they permit some imperative features where you want them. Examples include Standard ML, OCaml, F#, and Scala. You might consider one of these if you want to be able to get a grip on the functional idiomatic style while still being able to achieve things in reasonably familiar ways.
I've used Standard ML extensively in the past, but if you're looking for something that has a bit less of a learning curve, I'd personally recommend Scala, which is my second-favourite programming language. The reasons for this include the prevalence of libraries, a healthy-sized community, and the availability of nice books and tutorials to help you getting started (particularly if you have ever had any dealings with Java).
One element that was not discussed is the availability of special pattern-matching syntax for algebraic datatypes, as in Haskell, all flavors of ML, and probably several of the other languages mentioned. Pattern-matching syntax tends to help the programmer see their functions as mathematical functions. Haskell's syntax is sufficiently complex, and its implementations have sufficiently poor parse error messages, that syntax is a decent reason not to choose Haskell. Scheme is probably easier to learn than most other options (and Scheme probably has the king of all macro systems), but the lack of pattern matching syntax would steer me away from it for an intro to functional programming.
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Software Engineering as it is taught today is entirely focused on object-oriented programming and the 'natural' object-oriented view of the world. There is a detailed methodology that describes how to transform a domain model into a class model with several steps and a lot of (UML) artifacts like use-case-diagrams or class-diagrams. Many programmers have internalized this approach and have a good idea about how to design an object-oriented application from scratch.
The new hype is functional programming, which is taught in many books and tutorials. But what about functional software engineering?
While reading about Lisp and Clojure, I came about two interesting statements:
Functional programs are often developed bottom up instead of top down ('On Lisp', Paul Graham)
Functional Programmers use Maps where OO-Programmers use objects/classes ('Clojure for Java Programmers', talk by Rich Hickley).
So what is the methodology for a systematic (model-based ?) design of a functional application, i.e. in Lisp or Clojure? What are the common steps, what artifacts do I use, how do I map them from the problem space to the solution space?
Thank God that the software-engineering people have not yet discovered functional programming. Here are some parallels:
Many OO "design patterns" are captured as higher-order functions. For example, the Visitor pattern is known in the functional world as a "fold" (or if you are a pointy-headed theorist, a "catamorphism"). In functional languages, data types are mostly trees or tuples, and every tree type has a natural catamorphism associated with it.
These higher-order functions often come with certain laws of programming, aka "free theorems".
Functional programmers use diagrams much less heavily than OO programmers. Much of what is expressed in OO diagrams is instead expressed in types, or in "signatures", which you should think of as "module types". Haskell also has "type classes", which is a bit like an interface type.
Those functional programmers who use types generally think that "once you get the types right; the code practically writes itself."
Not all functional languages use explicit types, but the How To Design Programs book, an excellent book for learning Scheme/Lisp/Clojure, relies heavily on "data descriptions", which are closely related to types.
So what is the methodology for a systematic (model-based ?) design of a functional application, i.e. in Lisp or Clojure?
Any design method based on data abstraction works well. I happen to think that this is easier when the language has explicit types, but it works even without. A good book about design methods for abstract data types, which is easily adapted to functional programming, is Abstraction and Specification in Program Development by Barbara Liskov and John Guttag, the first edition. Liskov won the Turing award in part for that work.
Another design methodology that is unique to Lisp is to decide what language extensions would be useful in the problem domain in which you are working, and then use hygienic macros to add these constructs to your language. A good place to read about this kind of design is Matthew Flatt's article Creating Languages in Racket. The article may be behind a paywall. You can also find more general material on this kind of design by searching for the term "domain-specific embedded language"; for particular advice and examples beyond what Matthew Flatt covers, I would probably start with Graham's On Lisp or perhaps ANSI Common Lisp.
What are the common steps, what artifacts do I use?
Common steps:
Identify the data in your program and the operations on it, and define an abstract data type representing this data.
Identify common actions or patterns of computation, and express them as higher-order functions or macros. Expect to take this step as part of refactoring.
If you're using a typed functional language, use the type checker early and often. If you're using Lisp or Clojure, the best practice is to write function contracts first including unit tests—it's test-driven development to the max. And you will want to use whatever version of QuickCheck has been ported to your platform, which in your case looks like it's called ClojureCheck. It's an extremely powerful library for constructing random tests of code that uses higher-order functions.
For Clojure, I recommend going back to good old relational modeling. Out of the Tarpit is an inspirational read.
Personally I find that all the usual good practices from OO development apply in functional programming as well - just with a few minor tweaks to take account of the functional worldview. From a methodology perspective, you don't really need to do anything fundamentally different.
My experience comes from having moved from Java to Clojure in recent years.
Some examples:
Understand your business domain / data model - equally important whether you are going to design an object model or create a functional data structure with nested maps. In some ways, FP can be easier because it encourages you to think about data model separately from functions / processes but you still have to do both.
Service orientation in design - actually works very well from a FP perspective, since a typical service is really just a function with some side effects. I think that the "bottom up" view of software development sometimes espoused in the Lisp world is actually just good service-oriented API design principles in another guise.
Test Driven Development - works well in FP languages, in fact sometimes even better because pure functions lend themselves extremely well to writing clear, repeatable tests without any need for setting up a stateful environment. You might also want to build separate tests to check data integrity (e.g. does this map have all the keys in it that I expect, to balance the fact that in an OO language the class definition would enforce this for you at compile time).
Prototying / iteration - works just as well with FP. You might even be able to prototype live with users if you get very extremely good at building tools / DSL and using them at the REPL.
OO programming tightly couples data with behavior. Functional programming separates the two. So you don't have class diagrams, but you do have data structures, and you particularly have algebraic data types. Those types can be written to very tightly match your domain, including eliminating impossible values by construction.
So there aren't books and books on it, but there is a well established approach to, as the saying goes, make impossible values unrepresentable.
In so doing, you can make a range of choices about representing certain types of data as functions instead, and conversely, representing certain functions as a union of data types instead so that you can get, e.g., serialization, tighter specification, optimization, etc.
Then, given that, you write functions over your adts such that you establish some sort of algebra -- i.e. there are fixed laws which hold for these functions. Some are maybe idempotent -- the same after multiple applications. Some are associative. Some are transitive, etc.
Now you have a domain over which you have functions which compose according to well behaved laws. A simple embedded DSL!
Oh, and given properties, you can of course write automated randomized tests of them (ala QuickCheck).. and that's just the beginning.
Object Oriented design isn't the same thing as software engineering. Software engineering has to do with the entire process of how we go from requirements to a working system, on time and with a low defect rate. Functional programming may be different from OO, but it does not do away with requirements, high level and detailed designs, verification and testing, software metrics, estimation, and all that other "software engineering stuff".
Furthermore, functional programs do exhibit modularity and other structure. Your detailed designs have to be expressed in terms of the concepts in that structure.
One approach is to create an internal DSL within the functional programming language of choice. The "model" then is a set of business rules expressed in the DSL.
See my answer to another post:
How does Clojure aproach Separation of Concerns?
I agree more needs to be written on the subject on how to structure large applications that use an FP approach (Plus more needs to be done to document FP-driven UIs)
While this might be considered naive and simplistic, I think "design recipes" (a systematic approach to problem solving applied to programming as advocated by Felleisen et al. in their book HtDP) would be close to what you seem to be looking for.
Here, a few links:
http://www.northeastern.edu/magazine/0301/programming.html
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.8371
I've recently found this book:
Functional and Reactive Domain Modeling
I think is perfectly in line with your question.
From the book description:
Functional and Reactive Domain Modeling teaches you how to think of the domain model in terms of pure functions and how to compose them to build larger abstractions. You will start with the basics of functional programming and gradually progress to the advanced concepts and patterns that you need to know to implement complex domain models. The book demonstrates how advanced FP patterns like algebraic data types, typeclass based design, and isolation of side-effects can make your model compose for readability and verifiability.
There is the "program calculation" / "design by calculation" style associated with Prof. Richard Bird and the Algebra of Programming group at Oxford University (UK), I don't think its too far-fetched to consider this a methodology.
Personally while I like the work produced by the AoP group, I don't have the discipline to practice design in this way myself. However that's my shortcoming, and not one of program calculation.
I've found Behavior Driven Development to be a natural fit for rapidly developing code in both Clojure and SBCL. The real upside of leveraging BDD with a functional language is that I tend to write much finer grain unit tests than I usually do when using procedural languages because I do a much better job of decomposing the problem into smaller chunks of functionality.
Honestly if you want design recipes for functional programs, take a look at the standard function libraries such as Haskell's Prelude. In FP, patterns are usually captured by higher order procedures (functions that operate on functions) themselves. So if a pattern is seen, often a higher order function is simply created to capture that pattern.
A good example is fmap. This function takes a function as an argument and applies it to all the "elements" of the second argument. Since it is part of the Functor type class, any instance of a Functor (such as a list, graph, etc...) may be passed as a second argument to this function. It captures the general behavior of applying a function to every element of its second argument.
Well,
Generally many Functional Programming Languages are used at universities for a long time for "small toy problems".
They are getting more popular now since OOP has difficulties with "paralel programming" because of "state".And sometime functional style is better for problem at hand like Google MapReduce.
I am sure that, when functioanl guys hit the wall [ try to implement systems bigger than 1.000.000 lines of code], some of them will come with new software-engineering methodologies with buzz words :-). They should answer the old question: How to divide system into pieces so that we can "bite" each pieces one at a time? [ work iterative, inceremental en evolutionary way] using Functional Style.
It is sure that Functional Style will effect our Object Oriented
Style.We "still" many concepts from Functional Systems and adapted to
our OOP languages.
But will functional programs will be used for such a big systems?Will they become main stream? That is the question.
And Nobody can come with realistic methodology without implementing such a big systems, making his-her hands dirty.
First you should make your hands dirty then suggest solution. Solutions-Suggestions without "real pains and dirt" will be "fantasy".
I understand very clearly the difference between functional and imperative programming techniques. But there's a widespread tendency to talk of "functional languages", and this really confuses me.
Of course some languages like Haskell are more hospitable to functional programming than other languages like C. But even the former does I/O (it just keeps it in a ghetto). And you can write functional programs in C (it's just absurdly harder). So maybe it's just a matter of degree.
Still, even as a matter of degree, what does it mean when someone calls Scheme a "functional language"? Most Scheme code I see is imperative. Is it just that Scheme makes it easy to write in a functional style if you want to? So too do Lua and Python. Are they "functional languages" too?
I'm (really) not trying to be a language cop. If this is just a loose way of talking, that's fine. I'm just trying to figure out whether it does have some definite meaning (even if it's a matter-of-degree meaning) that I'm not seeing.
Among people who study programming languages for a living, "functional programming language" is a pretty weakly bound term. There is a strong consensus that:
Any language that calls itself functional must support first-class, nested functions with lexical scoping rules.
A significant minority also reserve the term "functional language" for languages which are:
Pure (or side-effect-free, referentially transparent, see also)
as in languages like Agda, Clean, Coq, and Haskell.
Beyond that, what's considered a functional programming language is often a matter of intent, that is, whether is designers want it to be called "functional".
Perl and Smalltalk are examples of languages that support first-class functions but whose designers don't call them functional. Objective Caml is an example of a language that is called functional even though it has a full object system with inheritance and everything.
Languages that are called "functional" will tend to have features like the following (taken from Defining point of functional programming):
Anonymous functions (lambda expressions)
Recursion (more prominent as a result of purity)
Programming with expressions rather than statements (again, from purity)
Closures
Currying / partial application
Lazy evaluation
Algebraic data types and pattern matching
Parametric polymorphism (a.k.a. generics)
The more a particular programming language has syntax and constructs tailored to making the various programming features listed above easy/painless to express & implement, the more likely someone will label it a "functional language".
I would say that a functional language is any language that allows functional programming without undue pain.
I like #Randolpho's answer. With regards to features, I might cite the list here:
Defining point of functional programming
namely
Purity (a.k.a. immutability, eschewing side-effects, referential transparency)
Higher-order functions (e.g. pass a function as a parameter, return it as a result, define anonymous function on the fly as a lambda expression)
Laziness (a.k.a. non-strict evaluation, most useful/usable when coupled with purity)
Algebraic data types and pattern matching
Closures
Currying / partial application
Parametric polymorphism (a.k.a. generics)
Recursion (more prominent as a result of purity)
Programming with expressions rather than statements (again, from purity)
The more a particular programming language has syntax and constructs tailored to making the various FP features listed above easy/painless to express & implement, the more likely someone will label it a "functional language".
Jane Street's Brian Hurt wrote a very good article on this a while back. The basic definition he arrived at is that a functional programming language is a language that models the lambda calculus. Think about what languages are widely agreed to be functional and you'll see that this is a very practical definition.
Lisp was a primitive attempt to model the lambda calculus, so it fits this definition — though since most implementations don't stick very closely to the ideas of lambda calculus, they're generally considered to be mixed-paradigm or at best weakly functional.
This is also why a lot of people bristle at languages like Python being called functional. Python's general philosophy is unrelated to lambda calculus — it doesn't encourage this way of thinking at all — so it's not a functional language. It's a Turing machine with first-class functions. You can do functional-style programming in Python, yes, but the language does not have its roots in the same math that functional languages do. (Incidentally, Guido van Rossum himself agrees with this description of the language.)
A language (and platform) that promotes Functional Programming as a means of fully leveraging the capabilities of the said platform.
A language that makes it a lot harder to create functions with side effects than without side effects. The same counts for mutable/immutable data structures.
I think the same question can be asked about "OOP languages". After all, you can write object oriented programs in C (and it's not uncommon to do so). But C doesn't have any built-in language constructs to enable OOP. You have to do OOP "by hand" without much help from the compiler. That's why it's usually not considered an OOP language. I think this distinction can be applied to "functional languages", too: For example, it's not uncommon to write functional code in C++ (think about STL functions like std::count_if or std::transform). But C++ (for now) lacks built-in language features that enable functional programming, like lambdas. (Let's ignore boost::lambda for the sake of the argument.)
So, to answer your question, I'd say although it's possible to write function programs in each of these languages:
C is not a functional language (no built-in functional language constructs)
Scheme, Python and friends have functional constructs, so they're functional languages. But they also have imperative and OOP constructs, so they're usually referred to as "multi-paradigm" languages.
You can do functional style programming in any language. I try as much as possible.
Python, Linq all promote functional style programming.
A pure functional language like Haskell requires you to do all your computations using mathematical functions, functions that do not modify anything, they just return values.
In addition, functional languages typically allow you to write higher order functions, functions that take functions as arguments and/or return types.
Haskell for one have different types for functions with side-effects and those without.
That's a pretty good discriminating property for being a 100% functional language, at least IMHO.
I wrote a (pretty long) analysis once on why the term 'functional programming language' is meaningless which also tries to explain why for instance 'functions' in Haskell are completely different from 'functions' in Lisp or Python: http://blog.nihilarchitect.net/archives/289/on-functional-programming/
Things like 'map' or 'filter' are for a large part also implementable in C for instance.
In object-oriented programming, we might say the core concepts are:
encapsulation
inheritance,
polymorphism
What would that be in functional programming?
There's no community consensus on what are the essential concepts in functional programming. In
Why Functional Programming Matters (PDF), John Hughes argues that they are higher-order functions and lazy evaluation. In Wearing the Hair Shirt: A Retrospective on Haskell, Simon Peyton Jones says the real essential is not laziness but purity. Richard Bird would agree. But there's a whole crowd of Scheme and ML programmers who are perfectly happy to write programs with side effects.
As someone who has practiced and taught functional programming for twenty years, I can give you a few ideas that are widely believed to be at the core of functional programming:
Nested, first-class functions with proper lexical scoping are at the core. This means you can create an anonymous function at run time, whose free variables may be parameters or local variables of an enclosing function, and you get a value you can return, put into data structures, and so on. (This is the most important form of higher-order functions, but some higher-order functions (like qsort!) can be written in C, which is not a functional language.)
Means of composing functions with other functions to solve problems. Nobody does this better than John Hughes.
Many functional programmers believe purity (freedom from effects, including mutation, I/O, and exceptions) is at the core of functional programming. Many functional programmers do not.
Polymorphism, whether it is enforced by the compiler or not, is a core value of functional programmers. Confusingly, C++ programmers call this concept "generic programming." When polymorphism is enforced by the compiler it is generally a variant of Hindley-Milner, but the more powerful System F is also a powerful basis for functional languages. And with languages like Scheme, Erlang, and Lua, you can do functional programming without a static type system.
Finally, a large majority of functional programmers believe in the value of inductively defined data types, sometimes called "recursive types". In languages with static type systems these are generally known as "algebraic data types", but you will find inductively defined data types even in material written for beginning Scheme programmers. Inductively defined types usually ship with a language feature called pattern matching, which supports a very general form of case analysis. Often the compiler can tell you if you have forgotten a case. I wouldn't want to program without this language feature (a luxury once sampled becomes a necessity).
In computer science, functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It emphasizes the application of functions, in contrast to the imperative programming style, which emphasizes changes in state. Functional programming has its roots in the lambda calculus, a formal system developed in the 1930s to investigate function definition, function application, and recursion. Many functional programming languages can be viewed as embellishments to the lambda calculus. - Wikipedia
In a nutshell,
Lambda Calculus
Higher Order Functions
Immutability
No side-effects
Not directly an answer to your question, but I'd like to point out that "object-oriented" and functional programming aren't necessarily at odds. The "core concepts" you cite have more general counterparts which apply just as well to functional programming.
Encapsulation, more generally, is modularisation. All purely functional languages that I know of support modular programming. You might say that those languages implement encapsulation better than the typical "OO" variety, since side-effects break encapsulation, and pure functions have no side-effects.
Inheritance, more generally, is logical implication, which is what a function represents. The canonical subclass -> superclass relation is a kind of implicit function. In functional languages, this is expressed with type classes or implicits (I consider implicits to be the more general of these two).
Polymorphism in the "OO" school is achieved by means of subtyping (inheritance). There is a more general kind of polymorphism known as parametric polymorphism (a.k.a. generics), which you will find to be supported by pure-functional programming languages. Additionally, some support "higher kinds", or higher-order generics (a.k.a. type constructor polymorphism).
What I'm trying to say is that your "core concepts of OO" aren't specific to OO in any way. I, for one, would argue that there aren't any core concepts of OO, in fact.
Let me repeat the answer I gave at one discussion in the Bangalore Functional Programming group:
A functional program consists only of functions. Functions compute
values from their inputs. We can contrast this with imperative
programming, where as the program executes, values of mutable
locations change. In other words, in C or Java, a variable called X
refers to a location whose value change. But in functional
programming X is the name of a value (not a location). Any where that
X is in scope, it has the same value (i.e, it is referentially
transparent). In FP, functions are also values. They can be passed as
arguments to other functions. This is known as higher-order functional
programming. Higher-order functions let us model an amazing variety of
patterns. For instance, look at the map function in Lisp. It
represents a pattern where the programmer needs to do 'something' to
every element of a list. That 'something' is encoded as a function and
passed as an argument to map.
As we saw, the most notable feature of FP is it's side-effect
freeness. If a function does something more than computing a value
from it's input, then it is causing a side-effect. Such functions are
not allowed in pure FP. It is easy to test side-effect free functions.
There is no global state to set-up before running the test and there
is no global state to check after running the test. Each function can
be tested independently just by providing it's input and examining the
return value. This makes it easy to write automated tests. Another
advantage of side-effect freeness is that it gives you better control
on parallelism.
Many FP languages treat recursion and iteration correctly. They does this by
supporting something called tail-recursion. What tail-recursion is -
if a function calls itself, and it is the last thing it does, it
removes the current stack frame right away. In other words, if a
function calls itself tail-recursively a 1000 times, it does not grow
the stack a 1000 deep. This makes special looping constructs
unnecessary in these languages.
Lambda Calculus is the most boiled down version of an FP language.
Higher level FP languages like Haskell get compiled to Lambda
Calculus. It has only three syntactic constructs but still it is
expressive enough to represent any abstraction or algorithm.
My opinion is that FP should be viewed as a meta-paradigm. We can
write programs in any style, including OOP, using the simple
functional abstractions provided by the Lambda Calculus.
Thanks,
-- Vijay
Original discussion link: http://groups.google.co.in/group/bangalore-fp/browse_thread/thread/4c2cfa7985d7eab3
Abstraction, the process of making a function by parameterizing over some part of an expression.
Application, the process of evaluating a function by replacing its parameters with specific values.
At some level, that's all there is to it.
Though the question is older, thought of sharing my view as reference.
Core Concept in FP is "FUNCTION"
FP gives KISS(Keep It Simple Sxxxxx) programming paradigm (once you get the FP ideas, you will literally start hating the OO paradigm)
Here is my simple FP comparison with OO Design Patterns. Its my perspective of seeing FP and pls correct me if there is any discrepancy from actual.
By Logic Programming I mean the a sub-paradigm of declarative programming languages. Don't confuse this question with "What problems can you solve with if-then-else?"
A language like Prolog is very fascinating, and it's worth learning for the sake of learning, but I have to wonder what class of real-world problems is best expressed and solved by such a language. Are there better languages? Does logic programming exist by another name in more trendy programming languages? Is the cynical version of the answer a variant of the Python Paradox?
Prototyping.
Prolog is dynamic and has been for 50 years. The compiler is liberal, the syntax minimalist, and "doing stuff" is easy, fun and efficient. SWI-Prolog has a built-in tracer (debugger!), and even a graphical tracer. You can change the code on the fly, using make/0, you can dynamically load modules, add a few lines of code without leaving the interpreter, or edit the file you're currently running on the fly with edit(1). Do you think you've found a problem with the foobar/2 predicate?
?- edit(foobar).
And as soon as you leave the editor, that thing is going to be re-compiled. Sure, Eclipse does the same thing for Java, but Java isn't exactly a prototyping language.
Apart from the pure prototyping stuff, Prolog is incredibly well suited for translating a piece of logic into code. So, automatic provers and that type of stuff can easily be written in Prolog.
The first Erlang interpreter was written in Prolog - and for a reason, since Prolog is very well suited for parsing, and encoding the logic you find in parse trees. In fact, Prolog comes with a built-in parser! No, not a library, it's in the syntax, namely DCGs.
Prolog is used a lot in NLP, particularly in syntax and computational semantics.
But, Prolog is underused and underappreciated. Unfortunately, it seems to bear an academic or "unusable for any real purpose" stigma. But it can be put to very good use in many real-world applications involving facts and the computation of relations between facts. It is not very well suited for number crunching, but CS is not only about number crunching.
Since Prolog = Syntactic Unification + Backward chaining + REPL,
most places where syntactic unification is used is also a good use for Prolog.
Syntactic unification uses
AST transformations
Type Inference
Term rewriting
Theorem proving
Natural language processing
Pattern matching
Combinatorial test case generation
Extract sub structures from structured data such as an XML document
Symbolic computation i.e. calculus
Deductive databases
Expert systems
Artificial Intelligence
Parsing
Query languages
Constraint Logic Programming (CLP)
Many very good and well-suited use cases of logic programming have already been mentioned. I would like to complement the existing list with several tasks from an extremely important application area of logic programming:
Logic programming blends seamlessly, more seamlessly than other paradigms, with constraints, resulting in a framework called Constraint Logic Programming.
This leads to dedicated constraint solvers for different domains, such as:
CLP(FD) for integers
CLP(B) for Booleans
CLP(Q) for rational numbers
CLP(R) for floating point numbers.
These dedicated constraint solvers lead to several important use cases of logic programming that have not yeen been mentioned, some of which I show below.
When choosing a Prolog system, the power and performance of its constraint solvers are often among the deciding factors, especially for commercial users.
CLP(FD) — Reasoning over integers
In practice, CLP(FD) is one of the most imporant applications of logic programming, and is used to solve tasks from the following areas, among others:
scheduling
resource allocation
planning
combinatorial optimization
See clpfd for more information and several examples.
CLP(B) — Boolean constraints
CLP(B) is often used in connection with:
SAT solving
circuit verification
combinatorial counting
See clpb.
CLP(Q) — Rational numbers
CLP(Q) is used to solve important classes of problems arising in Operations Research:
linear programming
integer linear programming
mixed integer linear programming
See clpq.
One of the things Prolog gives you for free is a backtracking search algorithm -- you could implement it yourself, but if your problem is best solved by having that algorithm available, then it's nice to use it.
The two things I've seen it be good at is mathematical proofs and natural language understanding.
Prolog is ideal for non-numeric problems. This article gives a few examples of some applications of Prolog and it might help you understand the type of problems that it might solve.
Prolog is great at solving puzzles and the like. That said, in the domain of puzzle-solving it makes easy/medium puzzle-solving easier and complicated puzzle solving harder. Still, writing solvers for grid puzzles and the like such as Hexiom, Sudoku, or Nurikabe is not especially tough.
One simple answer is "build systems". The language used to build Makefiles (at least, the part to describe dependencies) is essentially a logic programming language, although not really a "pure" logic programming language.
Yes, Prolog has been around since 1972. It was invented by Alain Colmerauer with Philippe Roussel, based on Robert Kowalski's procedural interpretation of Horn clauses. Alain was a French computer scientist and professor at Aix-Marseille University from 1970 to 1995.
And Alain invented it to analyse Natural Language. Several successful prototypes were created by him and his "followers".
His own system Orbis to understand questions in English and French about the solar system. See his personal site.
Warren and Pereira's system Chat80 QA on world geography.
Today, IBM Watson is a contempory QA based on logic with a huge dose of statistics about real world phrases.
So you can imagine that's where it's strength is.
Retired in 2006, he remained active until he died in 2017. He was named Chevalier de la Legion d’Honneur by the French government in 1986.