Algorithms: Sequential, Parallel, and Distributed / Edition 1 available in Hardcover
- Pub. Date:
- Cengage Learning
Algorithms: Sequential, Parallel, and Distributed offers in-depth coverage of traditional and current topics in sequential algorithms, as well as a solid introduction to the theory of parallel and distributed algorithms. In light of the emergence of modern computing environments such as parallel computers, the Internet, and cluster and grid computing, it is important that computer science students be exposed to algorithms that exploit these technologies. Berman and Paul's text will teach students how to create new algorithms or modify existing algorithms, thereby enhancing students' ability to think independently.
|Edition description:||New Edition|
|Product dimensions:||7.62(w) x 9.26(h) x 1.66(d)|
About the Author
Kenneth A. Berman is a Professor of Computer Science and Engineering at the University of Cincinnati. He is a co-director of the Laboratory for Networks and Applied Graph Theory and coordinator of the UC research group Internet Computing and Information Science. He has published over 50 research papers, and has taught both undergraduate and graduate courses in networks and algorithms for many years.
Jerome L. Paul is a Professor of Computer Science and Engineering at the University of Cincinnati. He was head of UC's Computer Science Department for 10 years, and is currently the director of the Laboratory for Integrated Networked Computing (LINC). He has published over 30 research papers, and has taught both undergraduate and graduate courses in sequential and parallel algorithms for many years.
Table of Contents
Part 1: Introduction to Algorithms 1. Introduction to Preliminaries 2. Design and Analysis Fundamentals 3. Mathematical Tools for Algorithm Analysis 4. Trees and Applications to Algorithms 5. More on Sorting Algorithms 6. Probability and Average Complexity of Algorithms Part 2: Major Design Strategies 7. The Greedy Method 8. Divide-and-Conquer 9. Dynamic Programming 10. Backtracking and Branch-and-Bound Part 3: Graph and Network Algorithms 11. Graphs and Digraphs 12. Minimum Spanning Tree and Shortest-Path Algorithms 13. Graph Connectivity and Fault-Tolerance of Networks 14. Matching and Network Flow Algorithms Part 4: Parallel and Distributed Algorithms 15. Introduction to Parallel Algorithms and Architectures 16. Parallel Design Strategies 17. Internet Algorithms 18. Distributed Computation Algorithms 19. Distributed Network Algorithms Part 5: Special Topics 20. String Matching and Document Processing 21. Balanced Search Trees 22. The Fast Fourier Transform 23. Heuristic Search Strategies: A*-Search and Game Trees 24. Probabilistic and Randomized Algorithms 25. Lower-Bound Theory 26. NP-Complete Problems 27. Approximation Algorithms Appendices A: Mathematical Notation and Background B: Linear Data Structures C: Interpolating Asympotic Behavior D: Random Walks in Digraphs E: Elementary Probability Theory F: Examples of Message-Passing Interface Code G: Pseudocode Conventions