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001 978-3-319-09888-3
003 DE-He213
005 20200420220217.0
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008 140903s2014 gw | s |||| 0|eng d
020 _a9783319098883
_9978-3-319-09888-3
024 7 _a10.1007/978-3-319-09888-3
_2doi
050 4 _aQA76.9.A43
072 7 _aUMB
_2bicssc
072 7 _aCOM051300
_2bisacsh
082 0 4 _a005.1
_223
100 1 _aVrajitoru, Dana.
_eauthor.
245 1 0 _aPractical Analysis of Algorithms
_h[electronic resource] /
_cby Dana Vrajitoru, William Knight.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXII, 466 p. 245 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUndergraduate Topics in Computer Science,
_x1863-7310
505 0 _aIntroduction -- Mathematical Preliminaries -- Fundamental Notations in Analysis of Algorithms -- Recurrence Relations -- Deterministic Analysis of Algorithms -- Algorithms and Probabilities -- Finite Graph Algorithms -- Appendix: Probability Theory.
520 _aAnalysis of algorithms plays an essential role in the education and training of any serious programmer preparing to deal with real world applications. Practical Analysis of Algorithms introduces the essential concepts of algorithm analysis required by core undergraduate and graduate computer science courses, in addition to providing a review of the fundamental mathematical notions necessary to understand these concepts. Throughout the text, the explanations are aimed at the level of understanding of a typical upper-level student, and are accompanied by detailed examples and classroom-tested exercises. Topics and features: Includes numerous fully-worked examples and step-by-step proofs, assuming no strong mathematical background Describes the foundation of the analysis of algorithms theory in terms of the big-Oh, Omega, and Theta notations Examines recurrence relations, a very important tool used in the analysis of algorithms Discusses the concepts of basic operation, traditional loop counting, and best case and worst case complexities Reviews various algorithms of a probabilistic nature, and uses elements of probability theory to compute the average complexity of algorithms such as Quicksort Introduces a variety of classical finite graph algorithms, together with an analysis of their complexity Provides an appendix on probability theory, reviewing the major definitions and theorems used in the book This clearly-structured and easy-to-read textbook/reference applies a unique, practical approach suitable for professional short courses and tutorials, as well as for students of computer science. Dr. Dana Vrajitoru is an Associate Professor of Computer Science at Indiana University South Bend, IN, USA. Dr. William Knight is an Emeritus Associate Professor at the same institution.
650 0 _aComputer science.
650 0 _aComputer programming.
650 0 _aAlgorithms.
650 0 _aComputer logic.
650 1 4 _aComputer Science.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aLogics and Meanings of Programs.
650 2 4 _aProgramming Techniques.
650 2 4 _aAlgorithms.
700 1 _aKnight, William.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319098876
830 0 _aUndergraduate Topics in Computer Science,
_x1863-7310
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-09888-3
912 _aZDB-2-SCS
942 _cEBK
999 _c51696
_d51696