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Some programming languages provide a complex data type for complex number storage and arithmetic as a built-in (primitive) data type. A complex variable or value is usually represented as a pair of floating-point numbers. Languages that support a complex data type usually provide special syntax for building such values, and extend the basic arithmetic operations ('+', '−', '×', '÷') to act on them. These operations are usually translated by the compiler into a sequence of floating-point machine instructions or into library calls. Those languages may also provide support for other operations, such as formatting, equality testing, etc. As in mathematics, those languages often interpret a floating-point value as equivalent to a complex value with a zero imaginary part. The FORTRAN COMPLEX type. The C99 standard of the C programming language includes complex data types and complex-math functions in the standard library header . The C++ standard library provides a complex template class as well as complex-math functions in the header. The Go programming language has built-in types complex64 (each component is 32-bit float) and complex128 (each component is 64-bit float). The Perl core module provides support for complex numbers. Python provides the built-in complex type. Imaginary number literals can be specified by appending a "j". Complex-math functions are provided in the standard library module cmath. Ruby provides a class in the standard library module . OCaml supports complex numbers with the standard library module . Haskell supports complex numbers with the standard library module (previously called ). Mercury provides complex numbers with full operator overloading support in the extras distribution, using . Java does not have a standard complex number class, but there exist a number of incompatible free implementations of a complex number class: The Apache Commons Math library provides complex numbers for Java with its class. The JScience library has a number class.

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Publications associées (1)

Concepts associés (10)

Complex data type

Julia (langage)

Julia est un langage de programmation de haut niveau, performant et dynamique pour le calcul scientifique, avec une syntaxe familière aux utilisateurs d'autres environnements de développement similaires (Matlab, R, Scilab, Python, etc.). Il fournit un compilateur sophistiqué, un système de types dynamiques avec polymorphisme paramétré, une exécution parallèle distribuée, des appels directs de fonctions C, Fortran et Python.

Enregistrement (structure de données)

En programmation, un enregistrement est une structure de données qui rassemble plusieurs champs, ceux-ci contenant des valeurs qui peuvent être de types différents. Typiquement, le nombre de champ et leur séquence sont fixés. Les champs d'un enregistrement peuvent aussi être nommés "membres", en particulier dans la programmation orientée objet. Les champs peuvent encore être appelés "éléments", mais cela entraîne un risque de confusion avec les éléments d'une collection.

Interoperation between Miniboxing and Other Generics Translations

Milos Stojanovic

Generics allow programmers to design algorithms and data structures that operate in the same way regardless of the data used by abstracting over data types. Generics are useful as they improve the programmer’s productivity by raising the level of abstraction, which in turn leads to reducing code duplication and uniform interfaces. However, as data on the low-level comes in different shapes and sizes, it is not a trivial job of compiler to bridge the gap between the uniform interface and the non-uniform low level implementation. Different approaches are used for generics translation and all of them can be categorized either into homogeneous or heterogeneous group. The characteristic of homogeneous translations is that all different data representations are transformed into an identical representation and use the same low-level code for this purpose. In the heterogeneous translations, code is duplicated and adapted for each incompatible data type. From a programmer’s point of view, there should be no difference between a generic method or class compiled using some homogeneous or heterogeneous translation. Therefore, the programmer can combine different types of translations together on different parts of the code and the program has to be correct. But, as different generics translations are implemented in different ways, interoperation between them introduces noticeable slowdowns as values need to be converted to the foreign object’s desired representation, incurring significant performance losses. In this thesis, it will be explored why slowdowns happen when different translations interact together and proposed the ways how they can interoperate more efficiently. Proposed approaches are implemented and their effectiveness is presented by benchmarking the implementation.

2015