ALGORITHMS RICHARD JOHNSONBAUGH MARCUS SCHAEFER PDF

Algorithms , by Richard Johnsonbaugh and Marcus Schaefer, is intended for an upper-level undergraduate or graduate course in algorithms. The authors include applications of algorithms, examples, end-of-section exercises, end-of-chapter exercises, solutions to selected exercises, figures, references, and notes to help the reader 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 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.

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About this title 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. From the Back Cover: Algorithms is written for an introductory upper-level undergraduate or graduate course in 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 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 carefully developed and classroom-tested exercises, from routine to challenging. About one-third of the end-of-section exercises include solutions. Includes more than worked examples, which provide motivation, clarify concepts, and show how to develop algorithms, demonstrate applications of the theory, and elucidate proofs.

He has degrees in computer science and mathematics from the University of Oregon, Yale University, and the University of Illinois at Chicago.

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.

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