DOCSLIB.ORG

On the Expressive Power of Programming Languages for Generative Design

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
On the Expressive Power of Programming Languages for Generative Design On the Expressive Power of Programming Languages for Generative Design The Case of Higher-Order Functions António Leitão1, Sara Proença2 1,2INESC-ID, Instituto Superior Técnico, Universidade de Lisboa 1,2{antonio.menezes.leitao|sara.proenca}@ist.utl.pt The expressive power of a language measures the breadth of ideas that can be described in that language and is strongly dependent on the constructs provided by the language. In the programming language area, one of the constructs that increases the expressive power is the concept of higher-order function (HOF). A HOF is a function that accepts functions as arguments and/or returns functions as results. HOF can drastically simplify the programming activity, reducing the development effort, and allowing for more adaptable programs. In this paper we explain the concept of HOFs and its use for Generative Design. We then compare the support for HOF in the most used programming languages in the GD field and we discuss the pedagogy of HOFs. Keywords: Generative Design, Higher-Order Functions, Programming Languages INTRODUCTION ming language is Turing-complete, meaning that Generative Design (GD) involves the use of algo- almost all programming languages have the same rithms that compute designs (McCormack 2004, Ter- computational power. In what regards their expres- dizis 2003). In order to take advantage of comput- sive power, however, they can be very different. A ers, these algorithms must be implemented in a pro- given programming language can be textual, visual, gramming language. There are two important con- or both but, in any case, it must be expressive enough cepts concerning programming languages: compu- to allow the description of a large range of algo- tational power, which measures the complexity of rithms. the problems that can be described using the lan- Expressiveness is intuitively used to measure guage, and expressive power, which measures the how easy it is for a programming language to im- breadth of ideas that can be described using the lan- plement complex ideas. There are studies (Felleisen, guage. The expressive power is directly related to 1991) that showed that it is possible to rank lan- the human effort needed to describe those ideas in guages according to a formal definition of expres- a given programming language. sive power that captures the intuitive meaning of It is a known fact that any non-trivial program- the term. In this ranking, a language that supports, Design Tool 1 - Volume 1 - eCAADe 32 | 257 for example, user-defined functions, (e.g., FORTRAN), summation: is more expressive than one that does not supports X10 them (e.g., BASIC). This does not mean that there is a i2 = 1 + 4 + 9 + 16 + ::: + 81 + 100 (1) problem solvable in FORTRAN that cannot be solved i=1 in BASIC. What it does mean is that there are prob- Although rarely presented as such, summation is a lems that require larger implementation efforts from HOF: it accepts a function (in the previous example, a BASIC programmer than from a FORTRAN program- disguised as the expression i2) and the limits of a mer. numeric sequence (in the example, 1 and 10), and In this paper, we claim that higher-order func- computes the sum of the applications of the func- tions (HOFs) are an important and powerful program- tion to all the elements of the sequence. Using the ming language feature for GD. HOFs increase the ex- appropriateP mathematical notation, the example be- pressiveness of a language, reducing the program- 10 ! 2 comes i=1(i i ), where it is now obvious that mer's effort and improving code reusability. the summation function accepts another function as In the next sections we describe the expressive argument. This is also visible in the formal definition power allowed by HOFs, we illustrate the advantages of the summation operation: of its use in GD, and we analyse the level of support ( provided by the programming languages currently Xb 0 a > b being used for GD. Finally, we describe a pedagogic f = P (2) f(a) + b f a ≤ b approach for teaching HOFs in the realm of GD prob- a a+1 lems. Note, in the above definition, that the parameter f is used as a function, in f(a). HIGHER-ORDER FUNCTIONS Although ubiquitous in many branches of math- A HOF is a function that accepts functions as argu- ematics, HOFs are unjustly considered a complex ments and/or computes functions as results. As an topic that, as a result, is frequently not taught prop- example, consider the derivative operator D(f), that erly and is not fully supported in a large number of ! 2 accepts a function as argument, such as x x + programming languages. However, as we will show, 3x, and computes another function as result, in this HOFs are an important tool for GD: they dramatically ! case, x 2x + 3. This fact can be written as simplify scripting (McCullough, 2006) and they are ! 2 ! D(x x + 3x) = x 2x + 3. Note that the a very convenient representation for parameterized functions that we used as argument and result of geometry (Park and Holt, 2010). Programming using the derivative operator did not have a name. These HOFs is known as higher-order programming. unnamed function are known as anonymous func- As an example, consider a balcony in a build- tions and there is an alternative notation for them: ing. During the (generative) design process, a de- λx.2x + 3. This notation was proposed by the λ- signer might be more concerned about the shape Calculus (Barendregt 1984) and, as we will see, it is of the building than about the shape of the balcony adopted by several programming languages. Note, and, as a result, he might decide to implement a sim- however, that from the point of view of the deriva- plified balcony. This means that the formalization tive operator, there is no difference between anony- of the design in a programming language includes mous functions and named ones. For example, it is a parametrized definition for the building that de- well-known that the derivative of the sine function is pends on the parametrized definition of the balcony, the cosine function, a fact that we can express using as follows: named functions, namely: D(sin) = cos. For a different example, consider a typical balcony(...) = ... 258 | eCAADe 32 - Design Tool 1 - Volume 1 building(...) = facade, etc.), functions describing sequences of ele- ... ments, etc. This representation allows us to gener- wall(...) ate not only a building that is identical to the original ... concept, but also an infinite number of variations, ex- slab(...) pressing different choices for all the function param- ... eters, including those that represent other functions. balcony(...) ... HIGHER-ORDER FUNCTIONS IN GENERA- Later, when the designer turns his attention to the balcony, he might want to experiment several differ- TIVE DESIGN ent designs. In less expressive languages, his only op- In spite of the simplicity of the idea of HOFs, a (gen- tion is to redefine the balcony definition for each ex- erative) designer still needs to think about the best periment. In more expressive languages that support strategy for its use, which is strongly dependent on HOFs, a better solution is to transform the balcony the current design goals. Returning to the previous into a parameter of the building definition, which example, it is clear that it is not enough to say that then becomes a HOF, as follows: the function that creates the balcony is a functional parameter of the function that creates the building; building(...,balcony,...) = we also need to specify the communication protocol ... between both functions, namely, which information wall(...) is expected by the balcony function and which infor- ... slab(...) mation does it return to the building function. De- ... pending on the programming language being used, balcony(...) the designer might decide to provide part or the en- ... tirety of the expected information as arguments to the function. Additional information might be pro- It is now possible to simultaneously define different vided in global variables or using other language- balcony functions expressing different balcony de- specific mechanisms. For illustration purposes, we signs and treat them as plug-ins for the function that will consider that the balcony function expects the represents the building. spatial location of a corner of the balcony and the di- Although it might seem that there is little differ- mensions of the intended balcony. We will assume ence between these two approaches, only the sec- that the building function provides this information ond approach allows the designer to easily create a each time it calls the balcony function. building containing different balcony designs. More- Regarding the design of the balcony, the de- over, both the function that represents the building signer might want to experiment different mathe- as well as the functions that represent the balconies matical functions for its shape. This means that it are now less dependent from each other and can be should be possible to define another (higher-order) reused in different contexts. function that accepts the shape function and that In this paper, we claim that, besides balconies, computes a specialized balcony function that follows many other aspects of a design can be transformed that shape. We will now assume the existence of into (functional) parameters of HOFs. We illustrate this function and we will only consider the different our claim by describing the design of a complex shape functions that the designer might want to ex- building, more specifically, the Market Hall (Boranyak periment. 2010), represented by functions that accept, as argu- One possibility is the use of a sinusoidal shape. ments, functions describing the overall shape of the To this end, the designer might define a function- building, functions for the different elements (posts, Design Tool 1 - Volume 1 - eCAADe 32 | 259 Figure 1 Balconies generated using higher-order functions.
Load more

Recommended publications
  • Solving the Funarg Problem with Static Types IFL ’21, September 01–03, 2021, Online
    Solving the Funarg Problem with Static Types Caleb Helbling Fırat Aksoy [email protected] fi[email protected] Purdue University Istanbul University West Lafayette, Indiana, USA Istanbul, Turkey ABSTRACT downwards. The upwards funarg problem refers to the manage- The difficulty associated with storing closures in a stack-based en- ment of the stack when a function returns another function (that vironment is known as the funarg problem. The funarg problem may potentially have a non-empty closure), whereas the down- was first identified with the development of Lisp in the 1970s and wards funarg problem refers to the management of the stack when hasn’t received much attention since then. The modern solution a function is passed to another function (that may also have a non- taken by most languages is to allocate closures on the heap, or to empty closure). apply static analysis to determine when closures can be stack allo- Solving the upwards funarg problem is considered to be much cated. This is not a problem for most computing systems as there is more difficult than the downwards funarg problem. The downwards an abundance of memory. However, embedded systems often have funarg problem is easily solved by copying the closure’s lexical limited memory resources where heap allocation may cause mem- scope (captured variables) to the top of the program’s stack. For ory fragmentation. We present a simple extension to the prenex the upwards funarg problem, an issue arises with deallocation. A fragment of System F that allows closures to be stack-allocated. returning function may capture local variables, making the size of We demonstrate a concrete implementation of this system in the the closure dynamic.
    [Show full text]
  • Dynamic Variables
    Dynamic Variables David R. Hanson and Todd A. Proebsting Microsoft Research [email protected] [email protected] November 2000 Technical Report MSR-TR-2000-109 Abstract Most programming languages use static scope rules for associating uses of identifiers with their declarations. Static scope helps catch errors at compile time, and it can be implemented efficiently. Some popular languages—Perl, Tcl, TeX, and Postscript—use dynamic scope, because dynamic scope works well for variables that “customize” the execution environment, for example. Programmers must simulate dynamic scope to implement this kind of usage in statically scoped languages. This paper describes the design and implementation of imperative language constructs for introducing and referencing dynamically scoped variables—dynamic variables for short. The design is a minimalist one, because dynamic variables are best used sparingly, much like exceptions. The facility does, however, cater to the typical uses for dynamic scope, and it provides a cleaner mechanism for so-called thread-local variables. A particularly simple implementation suffices for languages without exception handling. For languages with exception handling, a more efficient implementation builds on existing compiler infrastructure. Exception handling can be viewed as a control construct with dynamic scope. Likewise, dynamic variables are a data construct with dynamic scope. Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052 http://www.research.microsoft.com/ Dynamic Variables Introduction Nearly all modern programming languages use static (or lexical) scope rules for determining variable bindings. Static scope can be implemented very efficiently and makes programs easier to understand. Dynamic scope is usu- ally associated with “older” languages; notable examples include Lisp, SNOBOL4, and APL.
    [Show full text]
  • Closure Joinpoints: Block Joinpoints Without Surprises
    Closure Joinpoints: Block Joinpoints without Surprises Eric Bodden Software Technology Group, Technische Universität Darmstadt Center for Advanced Security Research Darmstadt (CASED) [email protected] ABSTRACT 1. INTRODUCTION Block joinpoints allow programmers to explicitly mark re- In this work, we introduce Closure Joinpoints for As- gions of base code as \to be advised", thus avoiding the need pectJ [6], a mechanism that allows programmers to explicitly to extract the block into a method just for the sake of cre- mark any Java expression or sequence of statement as \to ating a joinpoint. Block joinpoints appear simple to define be advised". Closure Joinpoints are explicit joinpoints that and implement. After all, regular block statements in Java- resemble labeled, instantly called closures, i.e., anonymous like languages are constructs well-known to the programmer inner functions with access to their declaring lexical scope. and have simple control-flow and data-flow semantics. As an example, consider the code in Figure1, adopted Our major insight is, however, that by exposing a block from Hoffman's work on explicit joinpoints [24]. The pro- of code as a joinpoint, the code is no longer only called in grammer marked the statements of lines4{8, with a closure its declaring static context but also from within aspect code. to be \exhibited" as a joinpoint Transaction so that these The block effectively becomes a closure, i.e., an anonymous statements can be advised through aspects. Closure Join- function that may capture values from the enclosing lexical points are no first-class objects, i.e., they cannot be assigned scope.
    [Show full text]
  • CONS Should Not CONS Its Arguments, Or, a Lazy Alloc Is a Smart Alloc Henry G
    CONS Should not CONS its Arguments, or, a Lazy Alloc is a Smart Alloc Henry G. Baker Nimble Computer Corporation, 16231 Meadow Ridge Way, Encino, CA 91436, (818) 501-4956 (818) 986-1360 (FAX) November, 1988; revised April and December, 1990, November, 1991. This work was supported in part by the U.S. Department of Energy Contract No. DE-AC03-88ER80663 Abstract semantics, and could profitably utilize stack allocation. One would Lazy allocation is a model for allocating objects on the execution therefore like to provide stack allocation for these objects, while stack of a high-level language which does not create dangling reserving the more expensive heap allocation for those objects that references. Our model provides safe transportation into the heap for require it. In other words, one would like an implementation which objects that may survive the deallocation of the surrounding stack retains the efficiency of stack allocation for most objects, and frame. Space for objects that do not survive the deallocation of the therefore does not saddle every program with the cost for the surrounding stack frame is reclaimed without additional effort when increased flexibility--whether the program uses that flexibility or the stack is popped. Lazy allocation thus performs a first-level not. garbage collection, and if the language supports garbage collection The problem in providing such an implementation is in determining of the heap, then our model can reduce the amortized cost of which objects obey LIFO allocation semantics and which do not. allocation in such a heap by filtering out the short-lived objects that Much research has been done on determining these objects at can be more efficiently managed in LIFO order.
    [Show full text]
  • Automatically Achieving Elasticity in the Implementation of Programming Languages Michael Lee Horowitz
    Automatically Achieving Elasticity in the Implementation of Programming Languages Michael Lee Horowitz January 1988 CMU-CS-88- I04 Submitted to CarnegieMellon Universityin partial fulf'dlment of the requirements for the degree of Doctor of Philosophy in Computer Science. Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Copyright © 1988 Michael Lee Horowitz. All fights reserved. This research was sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976 under contract F33615-87-C-1499 and monitored by the: Avionics Laboratory Air Force Wright Aeronautical Laboratories Aeronautical Systems Division (AFSC) Wright-Patterson AFB, OHIO 45433-6543 The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government. Abstract Several developments in programming language design and software engineering lead to a re- examination of the role of binding times in programming language implementation. We can alleviate problems associated with these developments by using event-driven processing to determine translation binding times. Our ultimate goal is to clarify the effects of different designs on binding times and to reduce binding time concerns in the language design process. Determining the binding time of a translation action involves two concerns: the ordering of the action's execution relative to other actions and the translation phase in which the execution occurs (e.g. compile- time, link-time, or run-time). Some issues that affect these concerns include: how to handle forward references, language designs that require dynamic interpretation, the role of link-time in languages that allow separate compilation, and how to achieve elasticity.
    [Show full text]
  • Functional Programming Is Free
    Functional Programming is Free. Francis Sergeraert January 2015 Abstract A paper [5] has recently been published. This paper is faulty: 1) The standard requirements about the definition of an algorithm are not respected, 2) The main point in the complexity study, namely the functional programming component, is absent. The Editorial Board of the SIAM JC had been warned a confirmed publication would be openly commented, it is the role of this text1. 1 History of the subject. I started the subject of Effective Homology in 1984, trying first to implement the Edgar Brown’s “algorithm” [4] computing the homotopy groups of the finite simply connected simplicial sets. This idea was quickly understood as ridiculous; thirty years later, there remains difficult to hope for an actual implementation of this algorithm, much too complex. Other methods were soon imagined to overcome the essential difficulty, con- sisting in using the strange power of the functional programming. Julio Rubio’s contribution was essential to make the project succeed: he understood in 1989 that the best theoretical tool to organize the work is the so-called Basic Perturbation Lemma [9, 3], more and more often called now the Homological Perturbation The- orem, you may question Google(Galway perturbation) to see numerous examples. Using Julio’s idea, a program called EAT (Effective Algebraic Topology) was soon obtained in 1990, mainly devoted to the computation of the homology groups of iterated loop spaces, already allowing us to produce homology groups so far arXiv:1504.00527v1 [cs.PL] 2 Apr 2015 unreachable by the “standard” methods. In the years 1995-8, Xavier Dousson’s thesis was devoted to the calculation of homotopy groups; the same tools, functional programming combined with the homological perturbation theorem, were successfully applied to the so-called White- head and Postnikov towers, the most “elementary” methods to compute the ho- motopy groups.
    [Show full text]
  • CSE583: Programming Languages Functional Programming
    Functional programming: two weeks G Scheme – Gives a strong, language-based foundation CSE583: for functional programming Programming Languages – May be mostly review for some of you G Some theory David Notkin – Theoretical foundations and issues in 11 January 2000 functional programming [email protected] G ML http://www.cs.washington.edu/education/courses/583 – A modern basis for discussing key issues in functional programming University of Washington • CSE583 • D. Notkin © 2000 2 Scheme: a Lisp descendant Statically scoped (review) G A statically scoped and properly tail-recursive G A free variable in a proc fork(x,y:int){ function definition is proc snork(a:int){ dialect of the Lisp programming language proc cork() { bound to the variable developed by Steele and Sussman in the mid a := x + y; of the same name in 1970s } the closest enclosing x : int; – Embodies an executable version of the lamda block a := x + y; } calculus G Dynamically scoped: } G Intended to have an exceptionally clear and free variable bound to simple semantics with few different ways to nearest match on the form expressions call stack University of Washington • CSE583 • D. Notkin © 2000 3 University of Washington • CSE583 • D. Notkin © 2000 4 Tail recursive Scheme G The top level call (define member(e l) G Dynamically typed, strongly typed (cond returns a value G identical to that of (null l) #f Expression-oriented, largely side-effect- the bottom level (equal e (car l)) #t free call (member e (cdr l)) – Functional languages are expression, not G Who cares? )) statement-oriented, since the expression – Performance! G Once an element is a member, define the function computation it’s always a member – Scheme requires G List-oriented, garbage-collected heap- G tail recursion to be Once you start returning true, based implemented as you keep on returning true until efficiently as you unwind the whole recursive G Good compilers exist iteration call stack University of Washington • CSE583 • D.
    [Show full text]
  • Some History of Functional Programming Languages
    Some History of Functional Programming Languages D. A. Turner University of Kent & Middlesex University Abstract. We study a series of milestones leading to the emergence of lazy, higher order, polymorphically typed, purely functional program- ming languages. An invited lecture given at TFP12, St Andrews Univer- sity, 12 June 2012. Introduction A comprehensive history of functional programming languages covering all the major streams of development would require a much longer treatment than falls within the scope of a talk at TFP, it would probably need to be book length. In what follows I have, firstly, focussed on the developments leading to lazy, higher order, polymorphically typed, purely functional programming languages of which Haskell is the best known current example. Secondly, rather than trying to include every important contribution within this stream I focus on a series of snapshots at significant stages. We will examine a series of milestones: 1. Lambda Calculus (Church & Rosser 1936) 2. LISP (McCarthy 1960) 3. Algol 60 (Naur et al. 1963) 4. ISWIM (Landin 1966) 5. PAL (Evans 1968) 6. SASL (1973{83) 7. Edinburgh (1969{80) | NPL, early ML, HOPE 8. Miranda (1986) 9. Haskell (1992 . ) 1 The Lambda Calculus The lambda calculus (Church & Rosser 1936; Church 1941) is a typeless theory of functions. In the brief account here we use lower case letters for variables: a; b; c ··· and upper case letters for terms: A; B; C ···. A term of the calculus is a variable, e.g. x, or an application AB, or an abstraction λx.A for some variable x. In the last case λx.
    [Show full text]
  • An Extension of Lambda-Calculus for Functional Programming*
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector J. LOGIC PROGRAMMING 1984:3:241-251 241 AN EXTENSION OF LAMBDA-CALCULUS FOR FUNCTIONAL PROGRAMMING* GYORGY REVESZ D An implementation oriented modification of lambda-calculus is presented together with some additional conversion rules for list manipulations. The resulting set of axioms is very simple and provides for a theoretical foundation of the semantics of functional programming. At the same time it can be used directly for the implementation of a powerful graph-reduction algorithm. <i INTRODUCTION Here we assume familiarity with the basics of lambda-calculus and with the main ideas of functional programming as promulgated by Backus. [2] The efforts to implement functional languages have come up with various ideas and approaches. There are basically two different groups of efforts: The first group is trying to solve the problem by using lambda-calculus reduction or combinatory reduction for function evaluation. (See, for instance, [4] and 171.1 The second group is trying to use more specialized techniques tailored to the given functional language. (See, for instance, [5].) In both cases the function to be evaluated is usually represented as a graph (sometimes a tree) and thus, the function evaluation process can be seen as a graph manipulation. Each step of the function evaluation will be reflected by a correspond- ing change in the graph and the entire graph should eventually collapse to a single node if the function evaluates to a constant. Now, the question is how best to represent a function via a graph and then how to make each step in a reasonably efficient manner.
    [Show full text]
  • AN INTRODUCTION to FUNCTIONAL PROGRAMMING THROUGH LAMBDA CALCULUS Greg Michaelson
    AN INTRODUCTION TO FUNCTIONAL PROGRAMMING THROUGH LAMBDA CALCULUS Greg Michaelson Department of Computing and Electrical Engineering Heriot-Watt University Riccarton Campus Edinburgh EH14 4AS - 2 - Preface Overview This book aims to provide a gentle introduction to functional programming. It is based on the premises that functional programming provides pedagogic insights into many aspects of computing and offers practical techniques for general problem solving. The approach taken is to start with pure λ calculus, Alonzo Church's elegent but simple formalism for computation, and add syntactic layers for function de®nitions, booleans, integers, recursion, types, characters, lists and strings to build a highish level functional notation. Along the way, a variety of topics are discussed including arithmetic, linear list and binary tree processing, and alternative evaluation strategies. Finally, functional programming in Standard ML and COMMON LISP, using techniques developed throughout the book, are explored. The material is presented sequentially. Each chapter depends on previous chapters. Within chapters, substantial use is made of worked examples. Each chapter ends with exercises which are based directly on ideas and techniques from that chapter. Specimen answers are included at the end of the book. Readership This book is intended for people who have taken a ®rst course in an imperative programming language like Pascal, FORTRAN or C and have written programs using arrays and sub-programs. There are no mathematical prerequisites and no prior experience with functional programming is required. The material from this book has been taught to third year undergraduate Computer Science students and to post graduate Knowledge Based Systems MSc students. Approach This book does not try to present functional programming as a complete paradigm for computing.
    [Show full text]
  • Dynamic Rebinding for Marshalling and Update, Via Redex-Time and Destruct-Time Reduction
    JFP 18 (4): 437–502, 2008. c 2007 Cambridge University Press 437 doi:10.1017/S0956796807006600 First published online 17 October 2007 Printed in the United Kingdom Dynamic rebinding for marshalling and update, via redex-time and destruct-time reduction PETER SEWELL University of Cambridge (e-mail: [email protected]) GARETH STOYLE University of Cambridge (e-mail: [email protected]) MICHAEL HICKS University of Maryland, College Park (e-mail: [email protected]) GAVIN BIERMAN Microsoft Research, Cambridge (e-mail: [email protected]) KEITH WANSBROUGH Data Connection Ltd., Edinburgh (e-mail: [email protected]) Abstract Most programming languages adopt static binding, but for distributed programming an exclusive reliance on static binding is too restrictive: dynamic binding is required in various guises, for example, when a marshalled value is received from the network, containing identifiers that must be rebound to local resources. Typically, it is provided only by ad hoc mechanisms that lack clean semantics. In this paper, we adopt a foundational approach, developing core dynamic rebinding mechanisms as extensions to the simply typed call-by- value λ calculus. To do so, we must first explore refinements of the call-by-value reduction strategy that delay instantiation, to ensure computations make use of the most recent versions of rebound definitions. We introduce redex-time and destruct-time strategies. The latter forms the basis for a λmarsh calculus that supports dynamic rebinding of marshalled values, while remaining as far as possible statically typed. We sketch an extension of λmarsh with concurrency and communication, giving examples showing how wrappers for encapsulating untrusted code can be expressed.
    [Show full text]
  • Disadvantages of Purely Functional Programming Links
    Mehr Nächster Blog» Blog erstellen Anmelden Diese Website verwendet Cookies von Google, um ihre Dienste bereitzustellen, Anzeigen zu personalisieren und Zugriffe zu analysieren. Informationen darüber, wie du die Website verwendest, werden an Google WEITERE INFORMATIONEN OK weitergegeben. Durch die Nutzung dieser Website erklärst du dich damit einverstanden, dass sie Cookies verwendet. The Flying Frog Blog Monday, 30 May 2016 Disadvantages of purely functional programming Links I have been asked to elaborate on my answer on Quora so here Visual F# 2010 for Technical Computing goes: (book) The F#.NET Journal (on-line magazine) The OCaml Journal (on-line magazine) F# for Numerics (library) 1. There is no efficient purely functional unsorted dictionary F# for Visualization (library) or set OCaml for Scientists (book) FFT .NET (library) Since the 1990s the use of dictionaries in software has gone Time-Frequency Analysis (Mathematica through the roof. Dictionaries are now a stock collection type that library) every programmer expects to find in their standard library. High-Level Virtual Machine (HLVM) Smoke Vector Graphics (OCaml library) Purely functional or persistent data structures such as those found in Okasaki’s fabulous monograph on the subject can be a great tool. In particular, the persistence they offer means you can reuse old versions of collections without having to worry about mutation. Popular Posts In many cases (particularly for some kinds of problems such as logic programming and compiler writing) this can make solutions Disadvantages of purely functional shorter and clearer, partly because it makes backtracking trivial. programming However, persistence comes at a great cost in terms of I have been asked to elaborate on my performance: purely functional dictionaries are typically 10x slower answer on Quora so here goes: 1.
    [Show full text]