Algorithms and Theory of Computation Handbook, Volume 1
eBook - ePub

Algorithms and Theory of Computation Handbook, Volume 1

General Concepts and Techniques

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Algorithms and Theory of Computation Handbook, Volume 1

General Concepts and Techniques

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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Yes, you can access Algorithms and Theory of Computation Handbook, Volume 1 by Mikhail J. Atallah,Marina Blanton in PDF and/or ePUB format, as well as other popular books in Computer Science & Programming Algorithms. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. Editors
  9. Contributors
  10. 1 Algorithm Design and Analysis Techniques
  11. 2 Searching
  12. 3 Sorting and Order Statistics
  13. 4 Basic Data Structures∗
  14. 5 Topics in Data Structures
  15. 6 Multidimensional Data Structures for Spatial Applications*
  16. 7 Basic Graph Algorithms
  17. 8 Advanced Combinatorial Algorithms
  18. 9 Dynamic Graph Algorithms
  19. 10 External-Memory Algorithms and Data Structures
  20. 11 Average Case Analysis of Algorithms*
  21. 12 Randomized Algorithms*
  22. 13 Pattern Matching in Strings
  23. 14 Maxime Crochemore
  24. 15 General Pattern Matching
  25. 16 Computational Number Theory
  26. 17 Algebraic and Numerical Algorithms
  27. 18 Applications of FFT and Structured Matrices
  28. 19 Basic Notions in Computational Complexity
  29. 20 Formal Grammars and Languages
  30. 21 Computability
  31. 22 Complexity Classes
  32. 23 Reducibility and Completeness
  33. 24 Other Complexity Classes and Measures
  34. 25 Parameterized Algorithms
  35. 26 Computational Learning Theory*
  36. 27 Algorithmic Coding Theory
  37. 28 Parallel Computation: Models and Complexity Issues
  38. 29 Distributed Computing: A Glimmer of a Theory
  39. 30 Linear Programming∗
  40. 31 Integer Programming*
  41. 32 Convex Optimization
  42. 33 Simulated Annealing Techniques
  43. 34 Approximation Algorithms for NP-Hard Optimization Problems
  44. Index