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Algorithms and Theory of Computation Handbook, Volume 1

Name: Algorithms and Theory of Computation Handbook, Volume 1
Author: Mikhail J. Atallah, Marina Blanton

General Concepts and Techniques

Mikhail J. Atallah,

Marina Blanton,

988 pages
English
PDF
Available on iOS & Android

eBook - PDF

Algorithms and Theory of Computation Handbook, Volume 1

General Concepts and Techniques

Mikhail J. Atallah,

Marina Blanton,

Book details

Table of contents

Citations

About This Book

Algorithms and Theory of Computation Handbook, Second Edition: General Concepts and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many

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Information

Publisher

Chapman and Hall/CRC

Year

2009

ISBN

9781584888239

Edition

Topic

Computer Science

Subtopic

Programming Games

Index

Computer Science

Front cover
Contents
Preface
Editors
Contributors
Chapter 1. Algorithm Design and Analysis Techniques
Chapter 2. Searching
Chapter 3. Sorting and Order Statistics
Chapter 4. Basic Data Structures*
Chapter 5. Topics in Data Structures
Chapter 6. Multidimensional Data Structures for Spatial Applications*
Chapter 7. Basic Graph Algorithms
Chapter 8. Advanced Combinatorial Algorithms
Chapter 9. Dynamic Graph Algorithms
Chapter 10. External-Memory Algorithms and Data Structures
Chapter 11. Average Case Analysis of Algorithms*
Chapter 12. Randomized Algorithms*
Chapter 13. Pattern Matching in Strings
Chapter 14. Text Data Compression Algorithms
Chapter 15. General Pattern Matching
Chapter 16. Computational Number Theory
Chapter 17. Algebraic and Numerical Algorithms
Chapter 18. Applications of FFT and Structured Matrices
Chapter 19. Basic Notions in Computational Complexity
Chapter 20. Formal Grammars and Languages
Chapter 21. Computability
Chapter 22. Complexity Classes
Chapter 23. Reducibility and Completeness
Chapter 24. Other Complexity Classes and Measures
Chapter 25. Parameterized Algorithms
Chapter 26. Computational Learning Theory*
Chapter 27. Algorithmic Coding Theory
Chapter 28. Parallel Computation: Models and Complexity Issues
Chapter 29. Distributed Computing: A Glimmer of a Theory
Chapter 30. Linear Programming*
Chapter 31. Integer Programming*
Chapter 32. Convex Optimization
Chapter 33. Simulated Annealing Techniques
Chapter 34. Approximation Algorithms for NP-Hard Optimization Problems
Back cover