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Have you ever wondered how your GPS can find the fastest way to your destination, selecting one route from seemingly countless possibilities in mere seconds? How your credit card account number is protected when you make a purchase over the Internet? The answer is algorithms. And how do these mathematical formulations translate themselves into your GPS, your laptop, or your smart phone? This book offers an engagingly written guide to the basics of computer algorithms. In Algorithms Unlocked, Thomas Cormen -- coauthor of the leading college textbook on the subject -- provides a general explanation, with limited mathematics, of how algorithms enable computers to solve problems. Readers will learn what computer algorithms are, how to describe them, and how to evaluate them. They will discover simple ways to search for information in a computer; methods for rearranging information in a computer into a prescribed order ("sorting"); how to solve basic problems that can be modeled in a computer with a mathematical structure called a "graph" (useful for modeling road networks, dependencies among tasks, and financial relationships); how to solve problems that ask questions about strings of characters such as DNA structures; the basic principles behind cryptography; fundamentals of data compression; and even that there are some problems that no one has figured out how to solve on a computer in a reasonable amount of time.
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Computational experiments on algorithms can supplement theoretical analysis by showing what algorithms, implementations and speed-up methods work best for specific machines or problems. This book guides the reader through the nuts and bolts of the major experimental questions: What should I measure? What inputs should I test? How do I analyze the data? To answer these questions the book draws on ideas from algorithm design and analysis, computer systems, and statistics and data analysis. The wide-ranging discussion includes a tutorial on system clocks and CPU timers, a survey of strategies for tuning algorithms and data structures, a cookbook of methods for generating random combinatorial inputs, and a demonstration of variance reduction techniques. The book can be used by anyone who has taken a course or two in data structures and algorithms. A companion website, AlgLab (www.cs.amherst.edu/alglab) contains downloadable files, programs and tools for use in experimental projects.
Computer algorithms. --- Algorithmes. --- Mathematics. --- Math --- Science --- Algorithms --- Algorithmes
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Graph theory --- Algorithms --- Algorithmes --- Algebra
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Les algorithmes prennent une place grandissante dans la société, que ce soit pour des applications informatiques ou pour des usages en société (réseaux sociaux, moteurs de recherche, affectation post-bac, découpage électoral). Lorsque la théorie prend du retard sur la pratique, les méthodes risquent d’être appliquées avant qu’on ait compris leurs aspects fondamentaux, ce qui induit des risques de manipulation. La perspective algorithmique allie des considérations d’efficacité à une approche systématique des problèmes passant par différentes phases (modélisation, formalisation, résolution, application) au cours desquelles l’aléatoire joue un rôle important. Quand ils sont bien conçus, les algorithmes peuvent être un outil de transformation de la société et contribuer au bien social.
Algorithms --- Algorithmes --- Social aspects. --- Moral and ethical aspects. --- Aspect social --- Aspect moral --- Multidisciplinary --- algorithmique --- algorithmes --- informatique --- Informatique et sciences numériques
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Les représentations numériques 3D ont révolutionné notre compréhension du monde. Elles sont devenues indispensables pour simuler des opérations chirurgicales, créer de nouveaux modes d’expression artistique ou explorer les ressources naturelles. La géométrie algorithmique apparaît à l’intersection de la géométrie et de l’informatique. Comment échantillonner, représenter et traiter des formes géométriques complexes ? Comment offrir des garanties théoriques sur la qualité des approximations et la complexité des algorithmes ? Comment assurer la fiabilité et l’efficacité des programmes informatiques ? Ces questions se posent en dimensions 2 et 3, mais aussi en plus grandes dimensions, pour analyser par exemple les grandes masses de données essentielles à la science moderne.
Multidisciplinary --- informatique --- Informatique et sciences numériques --- sciences numériques --- géométrie des données --- géométrie algorithmique --- données massives --- algorithmes
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Les algorithmes existent depuis que l’humain essaie de calculer. Au Moyen Âge, leur exécution est déléguée à des machines. En 1936, Alan Turing propose une machine universelle, exécutant tous les algorithmes possibles et imaginables, et donne ainsi naissance à l’ordinateur et à l’informatique. L’invention des réseaux, à partir des années 1960, a permis d’aller encore plus loin avec l’informatique répartie, connectant des ordinateurs dans de grands réseaux comme Internet et des processeurs dans de petits réseaux à l’intérieur de chacun des ordinateurs. L’objectif était de créer une super-machine, indestructible et ultra-rapide. Mais la recherche de ces « super-pouvoirs » a entraîné la perte de l’universalité. L’algorithmique répartie étudie les conditions permettant de retrouver l’universalité de Turing, ou des formes d’universalités restreintes réalisables.
Multidisciplinary --- informatique --- sciences numériques --- informatique répartie --- algorithmique --- algorithmes --- Internet --- réseaux --- universalité --- ordinateur --- asynchronisme --- calcul distribué --- calculabilité
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Algorithms --- Electronic digital computers --- Programming --- Computer programming --- Computer algorithms --- Programmation (Informatique) --- Algorithmes --- Computer algorithms. --- Computer programming. --- Algoritmen.
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"Computer simulation is an indispensable research tool in modeling, understanding and predicting nanoscale phenomena. However, the advanced computer codes used by researchers are too complicated for graduate students wanting to understand computer simulations of physical systems. This book gives students the tools to develop their own codes. Describing advanced algorithms, the book is ideal for students in computational physics, quantum mechanics, atomic and molecular physics, and condensed matter theory. It contains a wide variety of practical examples of varying complexity to help readers at all levels of experience. An algorithm library in Fortran 90, available online at www.cambridge.org/9781107001701, implements the advanced computational approaches described in the text to solve physical problems"--
Nanostructures --- Physics --- Computer algorithms --- Physique --- Algorithmes --- Data processing --- Informatique --- Computer algorithms. --- Algorithms --- Nanoscience --- Data processing. --- Nanostructures - Data processing --- Physics - Data processing
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Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems in many areas of computer science and operations research, including propositional satisfiability, constraint satisfaction, routing, and scheduling. SLS algorithms have also become increasingly popular for solving challenging combinatorial problems in many application areas, such as e-commerce and bioinformatics.Hoos and Stützle offer the first systematic and unified treatment of SLS algorithms. In this groundbreaking new book, they examine the
Stochastic programming --- Algorithms --- Combinatorial analysis --- Programmation stochastique --- Algorithmes --- Analyse combinatoire --- 681.3*G13 --- Numerical linear algebra: conditioning; determinants; eigenvalues and eigenvectors; error analysis; linear systems; matrix inversion; pseudoinverses; singular value decomposition; sparse, structured, and very large systems (direct and iterative methods) --- Linear programming --- Combinatorics --- Algebra --- Mathematical analysis --- Algorism --- Arithmetic --- Foundations --- Algorithmes. --- Programmation stochastique. --- Analyse combinatoire. --- Algorithms. --- Combinatorial analysis. --- Stochastic programming.
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