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Efficient Algorithm Design

You're reading from   Efficient Algorithm Design Unlock the power of algorithms to optimize computer programming

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835886823
Length 360 pages
Edition 1st Edition
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Author (1):
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Masoud Makrehchi Masoud Makrehchi
Author Profile Icon Masoud Makrehchi
Masoud Makrehchi
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Foundations of Algorithm Analysis
2. Chapter 1: Introduction to Algorithm Analysis FREE CHAPTER 3. Chapter 2: Mathematical Induction and Loop Invariant for Algorithm Correctness 4. Chapter 3: Rate of Growth for Complexity Analysis 5. Chapter 4: Recursion and Recurrence Functions 6. Chapter 5: Solving Recurrence Functions 7. Part 2: Deep Dive in Algorithms
8. Chapter 6: Sorting Algorithms 9. Chapter 7: Search Algorithms 10. Chapter 8: Symbiotic Relationship between Sort and Search 11. Chapter 9: Randomized Algorithms 12. Chapter 10: Dynamic Programming 13. Part 3: Fundamental Data Structures
14. Chapter 11: Landscape of Data Structures 15. Chapter 12: Linear Data Structures 16. Chapter 13: Non-Linear Data Structures 17. Part 4: Next Steps
18. Chapter 14: Tomorrow’s Algorithms 19. Index 20. Other Books You May Enjoy

Heaps

A heap is a special type of binary tree that satisfies the heap property. In a heap, the parent node always follows a specific order relation with respect to its children. Heaps are commonly used in various algorithms, especially in sorting and priority queues, due to their efficient access to the minimum or maximum element.

There are two main types of heaps, based on the order property they follow:

  • Max-heap: In a max-heap, each node’s value is greater than or equal to the values of its children, with the largest element positioned at the root. Max-heaps are commonly used in algorithms that need efficient access to the maximum element, such as heapsort and priority queue implementations. In max-heap, the heap property is as follows: <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mi mathvariant="normal">T</mi><mi mathvariant="normal">y</mi><mi mathvariant="normal">p</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">q</mi><mi mathvariant="normal">u</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">t</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">h</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">e</mi><mo>.</mo></mrow></mrow></math>
    • <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mi>A</mi><mfenced open="[" close="]"><mi>i</mi></mfenced><mo>≥</mo><mi>A</mi><mfenced open="[" close="]"><mrow><mn>2</mn><mi>i</mi><mo>+</mo><mn>1</mn></mrow></mfenced></mrow></mrow></math>(left child)
    • <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>A</mml:mi><mml:mfenced open="[" close="]" separators="|"><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:mfenced><mml:mo>≥</mml:mo><mml:mi>A</mml:mi><mml:mfenced open="[" close="]" separators="|"><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi><mml:mo>+</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:mfenced></mml:math> (right child), where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>A</mml:mi></mml:math> is the array representation of the heap
  • Min-heap: In a min-heap, the value of each node is less than or equal to the values of its children. The smallest element is always at the root. Min-heaps...
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