Algorithm Engineering Bridging the Gap Between Algorithm Theory and Practice / [electronic resource] :
edited by Matthias Müller-Hannemann, Stefan Schirra.
- 1st ed. 2010.
- XVI, 513 p. 72 illus. online resource.
- Theoretical Computer Science and General Issues, 5971 2512-2029 ; .
- Theoretical Computer Science and General Issues, 5971 .
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.
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 a rising 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 and algorithmic 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.
9783642148668
10.1007/978-3-642-14866-8 doi
Computer programming.
Algorithms.
Machine theory.
Software engineering.
Computer simulation.
Artificial intelligence--Data processing.
Programming Techniques.
Algorithms.
Formal Languages and Automata Theory.
Software Engineering.
Computer Modelling.
Data Science.
QA76.6-76.66
005.11
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.
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 a rising 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 and algorithmic 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.
9783642148668
10.1007/978-3-642-14866-8 doi
Computer programming.
Algorithms.
Machine theory.
Software engineering.
Computer simulation.
Artificial intelligence--Data processing.
Programming Techniques.
Algorithms.
Formal Languages and Automata Theory.
Software Engineering.
Computer Modelling.
Data Science.
QA76.6-76.66
005.11