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  2. Abstract data type - Wikipedia

    en.wikipedia.org/wiki/Abstract_data_type

    In computer science, an abstract data type (ADT) is a mathematical model for data types, defined by its behavior from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations.

  3. Algorithmic efficiency - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_efficiency

    Implementation issues can also have an effect on efficiency, such as the choice of programming language, or the way in which the algorithm is actually coded, [6] or the choice of a compiler for a particular language, or the compilation options used, or even the operating system being used.

  4. Data Authentication Algorithm - Wikipedia

    en.wikipedia.org/wiki/Data_Authentication_Algorithm

    The Data Authentication Algorithm (DAA) is a former U.S. government standard for producing cryptographic message authentication codes. DAA is defined in FIPS PUB 113, [1] which was withdrawn on September 1, 2008. [citation needed] The algorithm is not considered secure by today's standards.

  5. Set (abstract data type) - Wikipedia

    en.wikipedia.org/wiki/Set_(abstract_data_type)

    The CoreFoundation APIs provide the CFSet and CFMutableSet types for use in C. Python has built-in set and frozenset types since 2.4, and since Python 3.0 and 2.7, supports non-empty set literals using a curly-bracket syntax, e.g.: {x, y, z}; empty sets must be created using set(), because Python uses {} to represent the empty dictionary.

  6. Analysis of algorithms - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_algorithms

    Since algorithms are platform-independent (i.e. a given algorithm can be implemented in an arbitrary programming language on an arbitrary computer running an arbitrary operating system), there are additional significant drawbacks to using an empirical approach to gauge the comparative performance of a given set of algorithms. Take as an example ...

  7. Algorithm characterizations - Wikipedia

    en.wikipedia.org/wiki/Algorithm_characterizations

    And there are algorithms, of modern and classical varieties, not covered directly by Turing's analysis, for example, algorithms that interact with their environments, algorithms whose inputs are abstract structures, and geometric or, more generally, non-discrete algorithms" (Blass-Gurevich (2003) p. 8, boldface added)

  8. Introduction to Algorithms - Wikipedia

    en.wikipedia.org/wiki/Introduction_to_Algorithms

    Each chapter focuses on an algorithm, and discusses its design techniques and areas of application. Instead of using a specific programming language, the algorithms are written in pseudocode. The descriptions focus on the aspects of the algorithm itself, its mathematical properties, and emphasize efficiency. [7]

  9. Algorithms + Data Structures = Programs - Wikipedia

    en.wikipedia.org/wiki/Algorithms_+_Data...

    Algorithms + Data Structures = Programs [1] is a 1976 book written by Niklaus Wirth covering some of the fundamental topics of system engineering, computer programming, particularly that algorithms and data structures are inherently related. For example, if one has a sorted list one will use a search algorithm optimal for sorted lists.