Synopsis
Filling the void left by other algorithms books, Algorithms and Data Structures provides an approach that emphasizes design techniques. The volume includes application of algorithms, examples, end-of-section exercises, end-of-chapter exercises, hints and solutions to selected exercises, figures and notes to help the reader master the design and analysis of algorithms. This volume covers data structures, searching techniques, divided-and-conquer sorting and selection, greedy algorithms, dynamic programming, text searching, computational algebra, P and NP and parallel algorithms. For those interested in a better understanding of algorithms.
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About the Author
Richard Johnsonbaugh is Professor Emeritus of Computer Science at DePaul University. He has degrees in computer science and mathematics from the University of Oregon, Yale University, and the University of Illinois at Chicago. He is the author of numerous articles and books, including Discrete Mathematics, Fifth Edition, and, with co-author Martin Kalin, Object-Oriented Programming in C++, Second Edition, Applications Programming in C++, and Applications Programming in ANSI C, Third Edition.
Marcus Schaefer is Assistant Professor of Computer Science at DePaul University. He holds degrees in computer science and mathematics from the University of Chicago and the Universitat Karlsruhe. He has authored and co-authored several articles on complexity theory, computability, and graph theory.
From the Back Cover
Algorithms is written for an introductory upper-level undergraduate or graduate course in algorithms. With/their many years of experience in teaching algorithms courses, Richard Johnsonbaugh and Marcus Schaefer include applications of algorithms, examples, end-of-section exercises, end-of-chapter exercises, solutions to selected exercises, and notes to help the reader understand and master algorithms.
Key Features- Links theory to real-world applications such as data compression, region-finding in digital pictures, cellular phone networks, and the implementation of agrep.
- Includes five chapters that emphasize design techniques: searching (including backtracking), divide and conquer, sorting, selection, the greedy method, and dynamic programming.
- Covers distributed algorithms—a topic recommended by the ACM (2001 report) for an undergraduate curriculum.
- Features a collection of techniques, including approximation, parameterization (a recent area of research), and use of heuristics, to deal with NP-complete problems.
- Contains more than 1450 carefully developed and classroom-tested exercises, from routine to challenging. About one-third of the end-of-section exercises include solutions.
- Provides a robust Companion Website that supplements the text by providing algorithm simulation software, PowerPoint® slides, late breaking news about algorithms, references about the book's topics, computer programs, and more.
- Includes more than 300 worked examples, which provide motivation, clarify concepts, and show how to develop algorithms, demonstrate applications of the theory, and elucidate proofs.
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