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Practical Analysis of Algorithms (Undergraduate Topics in Computer Science) - Softcover

 
9783319098876: Practical Analysis of Algorithms (Undergraduate Topics in Computer Science)

Synopsis

This book 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. 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; 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.

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About the Author

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.

From the Back Cover

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.

"About this title" may belong to another edition of this title.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book 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. 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; 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. 480 pp. Englisch. Seller Inventory # 9783319098876

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book 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. 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; 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. Seller Inventory # 9783319098876

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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides highly detailed yet easy to understand explanations and proofsPresents numerous practical examples throughout the textIntroduces the fundamental mathematics required, described from the point of view of a programmerProvi. Seller Inventory # 4498598

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