000			03427nam a22005415i 4500
001			u374882
003			SIRSI
005			20160812084256.0
007			cr nn 008mamaa
008			100729s2010 gw | s |||| 0|eng d
020			_a9783642148668 _9978-3-642-14866-8
040			_cMX-MeUAM
050		4	_aQA76.9.A43
082	0	4	_a005.1 _223
100	1		_aMüller-Hannemann, Matthias. _eeditor.
245	1	0	_aAlgorithm Engineering _h[recurso electrónico] : _bBridging the Gap between Algorithm Theory and Practice / _cedited by Matthias Müller-Hannemann, Stefan Schirra.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aXVI, 513 p. 72 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Computer Science, _x0302-9743 ; _v5971
505	0		_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.
520			_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.
650		0	_aComputer science.
650		0	_aSoftware engineering.
650		0	_aData structures (Computer science).
650		0	_aComputer software.
650		0	_aElectronic data processing.
650		0	_aComputer simulation.
650	1	4	_aComputer Science.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aMathematical Logic and Formal Languages.
650	2	4	_aSoftware Engineering.
650	2	4	_aSimulation and Modeling.
650	2	4	_aData Structures.
650	2	4	_aNumeric Computing.
700	1		_aSchirra, Stefan. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642148651
830		0	_aLecture Notes in Computer Science, _x0302-9743 ; _v5971
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-14866-8
596			_a19
942			_cLIBRO_ELEC
999			_c202762 _d202762