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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

Recursion tree as a visualization technique

The recursion tree method is a powerful technique for solving and visualizing recurrence functions, particularly those that arise in the analysis of divide-and-conquer algorithms. It involves visualizing the recursive process as a tree, where each node represents a subproblem, and the edges represent the recursive calls. By summing the costs at each level of the tree, we can determine the overall complexity of the algorithm.

Here’s the step-by-step explanation of the recursion tree method:

  1. Construct the recursion tree:
    • Begin by writing down the original recurrence function
    • Each node in the tree represents a call to the recurrence
    • The root of the tree corresponds to the original problem <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>T</mml:mi><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:math>
    • The children of a node represent the subproblems generated by the recursive calls
  2. Identify the costs:
    • Determine the cost at each node. This cost typically corresponds to the non-recursive work done at that step, often denoted as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>f</mml:mi><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:math>.
    • Write down...
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