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Algorithm Engineering: Bridging the Gap Between Algorithm Theory and Practice: Lecture Notes in Computer Science, cartea 5971

Editat de Matthias Müller-Hannemann, Stefan Schirra
en Limba Engleză Paperback – 5 aug 2010
Algorithms are essential building blocks of computer applications. However, advancements in computer hardware, which render traditional computer models more and more unrealistic, and an ever increasing demand for efficient solution to actual real world problems have led to arising gap between classical algorithm theory and algorithmics in practice.The emerging discipline of Algorithm Engineering aims at bridging this gap. Driven by concrete applications, Algorithm Engineering complements theory by the benefits of experimentation and puts equal emphasis on all aspects arising during a cyclic solution process ranging from realistic modeling, design, analysis, robust and efficient implementations to careful experiments.This tutorial - outcome of a GI-Dagstuhl Seminar held in Dagstuhl Castle in September 2006 - covers the essential aspects of this process in ten chapters on basic ideas, modeling and design issues, analysis of algorithms, realistic computer models, implementation aspects andalgorithmic software libraries, selected case studies, as well as challenges in Algorithm Engineering. Both researchers and practitioners in the field will find it useful as a state-of-the-art survey.
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Specificații

ISBN-13: 9783642148651
ISBN-10: 3642148654
Pagini: 532
Ilustrații: XVI, 513 p. 72 illus.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 0.79 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Professional/practitioner

Cuprins

1. Foundations of Algorithm Engineering.- 2. Modeling.- 3. Selected Design Issues.- 4. Analysis of Algorithms.- 5. Realistic Computer Models.- 6. Implementation Aspects.- 7. Libraries.- 8. Experiments.- 9. Case Studies.- 10. Challenges in Algorithm Engineering.

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