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010   $a3-642-14866-2
024 7 $a10.1007/978-3-642-14866-8
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035   $a(SSID)ssj0000446283
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035   $a(PQKBTitleCode)TC0000446283
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035   $a(PQKB)11518751
035   $a(DE-He213)978-3-642-14866-8
035   $a(MiAaPQ)EBC3065577
035   $a(PPN)149018509
035   $a(EXLCZ)992670000000036361
100   $a20100729d2010      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aAlgorithm Engineering$b[electronic resource] $eBridging the Gap Between Algorithm Theory and Practice /$fedited by Matthias Müller-Hannemann, Stefan Schirra
205   $a1st ed. 2010.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010.
215   $a1 online resource (XVI, 513 p. 72 illus.) 
225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v5971
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-642-14865-4 
320   $aIncludes bibliographical references (p. [454]-496) and index.
327   $a1. 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.
330   $aAlgorithms are essential building blocks of computer applications. However, advancements in computer hardware, which render traditional computer models more and more unrealistic, and an ever increasing demand for efficient solution to actual real world problems have led to a rising gap between classical algorithm theory and algorithmics in practice. The emerging discipline of Algorithm Engineering aims at bridging this gap. Driven by concrete applications, Algorithm Engineering complements theory by the benefits of experimentation and puts equal emphasis on all aspects arising during a cyclic solution process ranging from realistic modeling, design, analysis, robust and efficient implementations to careful experiments. This tutorial - outcome of a GI-Dagstuhl Seminar held in Dagstuhl Castle in September 2006 - covers the essential aspects of this process in ten chapters on basic ideas, modeling and design issues, analysis of algorithms, realistic computer models, implementation aspects and algorithmic software libraries, selected case studies, as well as challenges in Algorithm Engineering. Both researchers and practitioners in the field will find it useful as a state-of-the-art survey.
410  0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v5971
606   $aComputer programming
606   $aAlgorithms
606   $aMachine theory
606   $aSoftware engineering
606   $aComputer simulation
606   $aArtificial intelligence?Data processing
606   $aProgramming Techniques
606   $aAlgorithms
606   $aFormal Languages and Automata Theory
606   $aSoftware Engineering
606   $aComputer Modelling
606   $aData Science
615  0$aComputer programming.
615  0$aAlgorithms.
615  0$aMachine theory.
615  0$aSoftware engineering.
615  0$aComputer simulation.
615  0$aArtificial intelligence?Data processing.
615 14$aProgramming Techniques.
615 24$aAlgorithms.
615 24$aFormal Languages and Automata Theory.
615 24$aSoftware Engineering.
615 24$aComputer Modelling.
615 24$aData Science.
676   $a004.01/5181
702   $aMüller-Hannemann$b Matthias$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aSchirra$b Stefan$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910484573703321
996   $aAlgorithm engineering$91980326
997   $aUNINA