Articles liés à Big O notation: Asymptotic analysis, Function (mathematics),...
Big O notation: Asymptotic analysis, Function (mathematics), Pure mathematics, Computational complexity theory, Algorithm, Computational resource, Best, worst and average case, Analysis of algorithms - Couverture souple
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Synopsis
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online.In mathematics, computer science, and related fields, big O notation (also known as Big Oh notation, Landau notation, Bachmann–Landau notation, and asymptotic notation) describes the limiting behavior of a function when the argument tends towards a particular value or infinity, usually in terms of simpler functions. Big O notation allows its users to simplify functions in order to concentrate on their growth rates: different functions with the same growth rate may be represented using the same O notation. Although developed as a part of pure mathematics, this notation is now frequently also used in computational complexity theory to describe an algorithm''s usage of computational resources: the worst case or average case running time or memory usage of an algorithm is often expressed as a function of the length of its input using big O notation. This allows algorithm designers to predict the behavior of their algorithms and to determine which of multiple algorithms to use, in a way that is independent of computer architecture or clock rate. Big O notation is also used in many other fields to provide similar estimates.
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Présentation de l'éditeur
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online.In mathematics, computer science, and related fields, big O notation (also known as Big Oh notation, Landau notation, Bachmann–Landau notation, and asymptotic notation) describes the limiting behavior of a function when the argument tends towards a particular value or infinity, usually in terms of simpler functions. Big O notation allows its users to simplify functions in order to concentrate on their growth rates: different functions with the same growth rate may be represented using the same O notation. Although developed as a part of pure mathematics, this notation is now frequently also used in computational complexity theory to describe an algorithm''s usage of computational resources: the worst case or average case running time or memory usage of an algorithm is often expressed as a function of the length of its input using big O notation. This allows algorithm designers to predict the behavior of their algorithms and to determine which of multiple algorithms to use, in a way that is independent of computer architecture or clock rate. Big O notation is also used in many other fields to provide similar estimates.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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