Информатика, кибернетика и программирование
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Lecture 3. Programming paradigms1.
The word programming paradigm is used in several different, although related meanings in computer science.
Programming paradigm a pattern that serves as a school of thoughts for programming of computers.
Programming technique related to an algorithmic idea for solving a particular class of problems.
Programming style the way we express ourselves in a computer program. Related to elegance or lack of elegance
Programming culture the totality of programming behavior, which often is tightly related to a family of programming languages. The sum of a main paradigm, programming styles, and certain programming techniques.
There are four main programming paradigms and a number of minor programming paradigms.
Main programming paradigms:
Other possible programming paradigms:
3.1 Overview of the imperative paradigm.
The word “imperative” can be used both as an adjective and as a noun. As an adjective it means 'expressing a command or plea'. In other words, asking for something to be done. As a noun, an imperative is a command or an order.
Characteristics of the imperative paradigm:
Several names are used for the computational steps in an imperative language. The word statement is often used with the special computer science meaning “an elementary instruction in a source language”. The word instruction is another possibility. This word is preferably devoted to the computational steps performed at the machine level. The word “command” is used for the imperatives in a high level imperative programming language.
A procedure abstracts one or more actions to a procedure, which can be activated as a single action.
3.2 Overview of the functional paradigm
Evaluate an expression and use the resulting value for something.
Characteristics of the functional paradigm:
3.3 Overview of the logic paradigm
Answer a question via search for a solution.
Characteristics of the logic paradigm:
3.4 Overview of the object-oriented paradigm
Send messages between objects to simulate the temporal evolution of a set of real world phenomena.
Characteristics of the object-oriented paradigm:
3.5 The visual paradigm
The visual paradigm or visual programming is writing programs in a language which manipulates visual information or supports visual interaction. Visual programming could be:
Visual programming language (VPL). Any programming language that allows the user to specify a program in a two(or more)-dimensional way. Conventional textual languages are not considered two-dimensional since the compiler or interpreter processes them as one-dimensional streams of characters. A VPL allows programming with visual expressions - spatial arrangements of textual and graphical symbols.
VPLs may be further classified, according to the type and extent of visual expression used, into icon-based languages, form-based languages and diagram languages. Visual programming environments provide graphical or iconic elements which can be manipulated by the user in an interactive way according to some specific spatial grammar for program construction.
A visually transformed language is a non-visual language with a superimposed visual representation. Naturally visual languages have an inherent visual expression for which there is no obvious textual equivalent.
Visual Basic, Visual C++ and the entire Microsoft Visual family are not, despite their names, visual programming languages. They are textual languages which use a graphical GUI builder to make programming interfaces easier. The user interface portion of the programming environment is visual, the languages are not. Because of the confusion caused by the multiple meanings of the term “visual programming”, the term “executable graphics” has been proposed as an alternative to VPL.
Some examples of visual programming languages are Prograph, Pict, Tinkertoy, Fabrik, CODE 2.0 and Hyperpascal.
Visual programming environment. Software which allows the use of visual expressions (such as graphics, drawings, animation or icons) in the process of programming. These visual expressions may be used as graphical interfaces for textual programming languages. They may be used to form the syntax of new visual programming languages leading to new paradigms such as programming by demonstration or they may be used in graphical presentations of the behaviour or structure of a program.
3.6 Common parallel programming paradigms2
Parallel computing using a system such as parallel virtual machine (PVM) may be approached from three fundamental viewpoints, based on the organization of the computing tasks. Within each, different workload allocation strategies are possible.
The first and most common model for PVM applications can be termed “crowd” computing: a collection of closely related processes, typically executing the same code, perform computations on different portions of the workload, usually involving the periodic exchange of intermediate results. This paradigm can be further subdivided into two categories:
The master-slave (or host-node) model in which a separate “control” program termed the master is responsible for process spawning, initialization, collection and display of results, and perhaps timing of functions. The slave programs perform the actual computation involved; they either are allocated their workloads by the master (statically or dynamically) or perform the allocations themselves.
The node-only model where multiple instances of a single program execute, with one process (typically the one initiated manually) taking over the noncomputational responsibilities in addition to contributing to the computation itself.
The second model supported by PVM is termed a “tree”' computation. In this scenario, processes are spawned (usually dynamically as the computation progresses) in a tree-like manner, thereby establishing a tree-like, parent-child relationship (as opposed to crowd computations where a star-like relationship exists). This paradigm, although less commonly used, is an extremely natural fit to applications where the total workload is not known a priori, for example, in branch-and-bound algorithms, alpha-beta search, and recursive “divide-and-conquer”' algorithms.
The third model, termed “hybrid”, can be thought of as a combination of the tree model and crowd model. Essentially, this paradigm possesses an arbitrary spawning structure: that is, at any point during application execution, the process relationship structure may resemble an arbitrary and changing graph.
These three classifications are made on the basis of process relationships, though they frequently also correspond to communication topologies. Nevertheless, in all three, it is possible for any process to interact and synchronize with any other. Further, the choice of model is application dependent and should be selected to best match the natural structure of the parallelized program
3.7 The constraint based paradigm
In computer science, constraint programming is a programming paradigm wherein relations between variables are stated in the form of constraints. Constraints differ from the common primitives of imperative programming languages in that they do not specify a step or sequence of steps to execute, but rather the properties of a solution to be found. This makes constraint programming a form of declarative programming. The constraints used in constraint programming are of various kinds: those used in constraint satisfaction problems (e.g. “A or B is true”), those solved by the simplex algorithm (e.g. “x ≤ 5”), and others. Constraints are usually embedded within a programming language or provided via separate software libraries.
Constraint programming can be expressed in the form of constraint logic programming, which embeds constraints into a logic program. This variant of logic programming is due to Jaffar and Lassez, who extended in 1987 a specific class of constraints that were introduced in Prolog II. The first implementations of constraint logic programming were Prolog III, CLP(R), and CHIP.
Instead of logic programming, constraints can be mixed with functional programming, term rewriting, and imperative languages. Programming languages with built-in support for constraints include Oz (functional programming) and Kaleidoscope (imperative programming). Mostly, constraints are implemented in imperative languages via constraint solving toolkits, which are separate libraries for an existing imperative language.
The constraints used in constraint programming are typically over some specific domains. Some popular domains for constraint programming are:
Finite domains is one of the most successful domains of constraint programming. In some areas (like operations research) constraint programming is often identified with constraint programming over finite domains.