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The NIST Dictionary of Algorithms and Data Structures [1] is a reference work maintained by the U.S. National Institute of Standards and Technology.It defines a large number of terms relating to algorithms and data structures.
A data structure known as a hash table.. In computer science, a data structure is a data organization and storage format that is usually chosen for efficient access to data. [1] [2] [3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, [4] i.e., it is an algebraic structure about data.
A domain-specific architecture (DSA) is a programmable computer architecture specifically tailored to operate very efficiently within the confines of a given application domain. The term is often used in contrast to general-purpose architectures, such as CPUs , that are designed to operate on any computer program .
Python's name is derived from the British comedy group Monty Python, whom Python creator Guido van Rossum enjoyed while developing the language. Monty Python references appear frequently in Python code and culture; [ 190 ] for example, the metasyntactic variables often used in Python literature are spam and eggs instead of the traditional foo ...
The Digital Signature Algorithm (DSA) is a variant of the ElGamal signature scheme, which should not be confused with ElGamal encryption. ElGamal encryption can be defined over any cyclic group G {\displaystyle G} , like multiplicative group of integers modulo n if and only if n is 1, 2, 4, p k or 2 p k , where p is an odd prime and k > 0 .
In computer science, a deterministic algorithm is an algorithm that, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states.
The size of the container depends on the number of objects (elements) it contains. Underlying (inherited) implementations of various container types may vary in size, complexity and type of language, but in many cases they provide flexibility in choosing the right implementation for any given scenario.
Standard examples of data-driven languages are the text-processing languages sed and AWK, [1] and the document transformation language XSLT, where the data is a sequence of lines in an input stream – these are thus also known as line-oriented languages – and pattern matching is primarily done via regular expressions or line numbers.