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Algorithms and Data Structures: Foundations and Probabilistic Methods for Design and Analysis Paperback - 2021
by Knebl, Helmut
- Used
- very good
- Paperback
A$67.79
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Details
- Title Algorithms and Data Structures: Foundations and Probabilistic Methods for Design and Analysis
- Author Knebl, Helmut
- Binding Paperback
- Condition Used - Very good
- Pages 349
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 11/2/2021 12:00:01 A
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # mon0003678628
- ISBN 9783030597603 / 3030597601
- Weight 1.12 lbs (0.51 kg)
- Dimensions 9.21 x 6.14 x 0.75 in (23.39 x 15.60 x 1.91 cm)
- Size 0.7480 9.1339 6.1417
- Category Computers - Languages / Programming
- Quantity available 1
- Bookseller catalogues Book
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From the rear cover
This is a central topic in any computer science curriculum. To distinguish this textbook from others, the author considers probabilistic methods as being fundamental for the construction of simple and efficient algorithms, and in each chapter at least one problem is solved using a randomized algorithm. Data structures are discussed to the extent needed for the implementation of the algorithms. The specific algorithms examined were chosen because of their wide field of application.
This book originates from lectures for undergraduate and graduate students. The text assumes experience in programming algorithms, especially with elementary data structures such as chained lists, queues, and stacks. It also assumes familiarity with mathematical methods, although the author summarizes some basic notations and results from probability theory and related mathematical terminology in the appendices. He includes many examples to explain the individual steps of the algorithms, and he concludes each chapter with numerous exercises.
This book originates from lectures for undergraduate and graduate students. The text assumes experience in programming algorithms, especially with elementary data structures such as chained lists, queues, and stacks. It also assumes familiarity with mathematical methods, although the author summarizes some basic notations and results from probability theory and related mathematical terminology in the appendices. He includes many examples to explain the individual steps of the algorithms, and he concludes each chapter with numerous exercises.
