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Data Structures and Algorithms with the C++ STL

You're reading from   Data Structures and Algorithms with the C++ STL A guide for modern C++ practitioners

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781835468555
Length 458 pages
Edition 1st Edition
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John Farrier John Farrier
Author Profile Icon John Farrier
John Farrier
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Table of Contents (30) Chapters Close

Preface 1. Part 1: Mastering std::vector
2. Chapter 1: The Basics of std::vector FREE CHAPTER 3. Chapter 2: Mastering Iterators with std::vector 4. Chapter 3: Mastering Memory and Allocators with std::vector 5. Chapter 4: Mastering Algorithms with std::vector 6. Chapter 5: Making a Case for std::vector 7. Part 2: Understanding STL Data Structures
8. Chapter 6: Advanced Sequence Container Usage 9. Chapter 7: Advanced Ordered Associative Container Usage 10. Chapter 8: Advanced Unordered Associative Container Usage 11. Chapter 9: Advanced Container Adaptor Usage 12. Chapter 10: Advanced Container View Usage 13. Part 3: Mastering STL Algorithms
14. Chapter 11: Fundamental Algorithms and Searching 15. Chapter 12: Manipulation and Transformation 16. Chapter 13: Numeric and Range -Based Operations 17. Chapter 14: Permutations, Partitions, and Heaps 18. Chapter 15: STL with Ranges 19. Part 4: Creating STL-Compatible Types and Algorithms
20. Chapter 16: Creating STL-Types Containers 21. Chapter 17: Creating STL -Compatible Algorithms 22. Chapter 18: Type Traits and Policies 23. Part 5: STL Data Structures and Algorithms: Under the Hood
24. Chapter 19: Exception Safety 25. Chapter 20: Thread Safety and Concurrency with the STL 26. Chapter 21: STL Interaction with Concepts and Coroutines 27. Chapter 22: Parallel Algorithms with the STL 28. Index 29. Other Books You May Enjoy

Best practices

The following best practices are associated with numeric and range-based operations:

  • Selecting the most suitable algorithm based on data attributes: Effectively using the numeric and range-based operations from the C++ STL requires more than just knowing their functionalities; it’s crucial to discern when and where to employ them to ensure optimal performance and data preservation. Different datasets exhibit varying properties such as size, distribution, and repetition frequency. These properties can influence the performance of certain operations. For example, std::stable_sort may prove more efficient than other sorting methods for a nearly sorted dataset. Thus, understanding your dataset’s characteristics is pivotal when deciding appropriate operations.
  • Maintaining data integrity in sorted operations: Preserving data integrity is paramount, especially in processes that rely on sorted datasets. Many range-based operations, such as binary searches...
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