Treffer: Practical Analysis of Algorithms

Title:
Practical Analysis of Algorithms
Contributors:
Knight, William, author.
Publication Year:
2014
Physical Description:
XII, 466 p. 245 illus. online resource.
Series:
Undergraduate Topics in Computer Science
Undergraduate Topics in Computer Science.
Contents Note:
Introduction -- Mathematical Preliminaries -- Fundamental Notations in Analysis of Algorithms -- Recurrence Relations -- Deterministic Analysis of Algorithms -- Algorithms and Probabilities -- Finite Graph Algorithms -- Appendix: Probability Theory.
Original Identifier:
(Springer)9783319098883
Document Type:
Buch Book
Language:
English
ISBN:
978-3-319-09888-3
978-3-319-09887-6
3-319-09888-8
3-319-09887-X
Rights:
This record is part of the Harvard Library Bibliographic Dataset, which is provided by the Harvard Library under its Bibliographic Dataset Use Terms and includes data made available by, among others, OCLC Online Computer Library Center, Inc. and the Library of Congress.
Accession Number:
edshlc.014199176.3
Database:
Harvard Library Bibliographic Dataset

Weitere Informationen

Analysis 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.