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by Kenneth Berman and Jerome Paul

Edition: 05Copyright: 2005

Publisher: Course Technology, Inc.

Published: 2005

International: No

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Reflecting the increasing importance of parallel algorithms and parallel computer architectures, Fundamentals of Sequential and Parallel Algorithms provides in-depth coverage of traditional and current topics in sequential algorithms, as well as a solid foundation in the theory of parallel algorithms. Professors Berman and Paul give students a comprehensive toolkit of sequential and parallel algorithms plus a set of mathematical techniques for assessing the performance and correctness of algorithms. Their balanced, integrated presentation of sequential and parallel algorithms helps students gain a more intuitive ability to select appropriate algorithms from a variety of alternatives. The authors determine worst, best, and average running times for the algorithms covered. For both Parallel Random Access Machines and interconnection network models, they present algorithms in parallel pseudocode.

Part 1: Introduction to Algorithms

1. Algorithms from Ancient to Modern Times

2. Design and Analysis Fundamentals

3. Mathematical Tools for Algorithm Analysis

4. Trees and Applications to Algorithms

5. More on Sorting Algorithms

6. Probabilistic Analysis 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. Searching and the Internet

18. String Matching and Document Processing

19. Distributed Computing Algorithms

20. Distributed Network Algorithms

Part 5: Special Topics

21. Balanced Search Trees

22. The Fourier Transform

23. Heuristic Search Strategies

24. Probabilistic and Randomized Algorithms

25. Lower Bound Theory

26. NP-Complete Problems and the Class NC

27. Approximation Algorithms

Appendices

A: Mathematical Notation and Background

B: Linear Data Structures

C: Mathematical Induction

D: Solving Recurrence Relations

E: Elementary Probability Theory

F: MPI Examples

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Summary

Reflecting the increasing importance of parallel algorithms and parallel computer architectures, Fundamentals of Sequential and Parallel Algorithms provides in-depth coverage of traditional and current topics in sequential algorithms, as well as a solid foundation in the theory of parallel algorithms. Professors Berman and Paul give students a comprehensive toolkit of sequential and parallel algorithms plus a set of mathematical techniques for assessing the performance and correctness of algorithms. Their balanced, integrated presentation of sequential and parallel algorithms helps students gain a more intuitive ability to select appropriate algorithms from a variety of alternatives. The authors determine worst, best, and average running times for the algorithms covered. For both Parallel Random Access Machines and interconnection network models, they present algorithms in parallel pseudocode.

Table of Contents

Part 1: Introduction to Algorithms

1. Algorithms from Ancient to Modern Times

2. Design and Analysis Fundamentals

3. Mathematical Tools for Algorithm Analysis

4. Trees and Applications to Algorithms

5. More on Sorting Algorithms

6. Probabilistic Analysis 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. Searching and the Internet

18. String Matching and Document Processing

19. Distributed Computing Algorithms

20. Distributed Network Algorithms

Part 5: Special Topics

21. Balanced Search Trees

22. The Fourier Transform

23. Heuristic Search Strategies

24. Probabilistic and Randomized Algorithms

25. Lower Bound Theory

26. NP-Complete Problems and the Class NC

27. Approximation Algorithms

Appendices

A: Mathematical Notation and Background

B: Linear Data Structures

C: Mathematical Induction

D: Solving Recurrence Relations

E: Elementary Probability Theory

F: MPI Examples

Publisher Info

Publisher: Course Technology, Inc.

Published: 2005

International: No

Published: 2005

International: No

1. Algorithms from Ancient to Modern Times

2. Design and Analysis Fundamentals

3. Mathematical Tools for Algorithm Analysis

4. Trees and Applications to Algorithms

5. More on Sorting Algorithms

6. Probabilistic Analysis 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. Searching and the Internet

18. String Matching and Document Processing

19. Distributed Computing Algorithms

20. Distributed Network Algorithms

Part 5: Special Topics

21. Balanced Search Trees

22. The Fourier Transform

23. Heuristic Search Strategies

24. Probabilistic and Randomized Algorithms

25. Lower Bound Theory

26. NP-Complete Problems and the Class NC

27. Approximation Algorithms

Appendices

A: Mathematical Notation and Background

B: Linear Data Structures

C: Mathematical Induction

D: Solving Recurrence Relations

E: Elementary Probability Theory

F: MPI Examples