Grokking Algorithm Complexity and Big-O
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Introduction to Recursion
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Recursive algorithms solve problems by breaking them down into smaller instances of the same problem. In recursion, a function calls itself with a reduced input until reaching a base case, which ends the recursion. This approach is often elegant and mirrors the natural structure of problems that can be divided into subproblems, such as calculating factorials, performing searches in trees, or solving complex mathematical problems.

When it comes to analyzing recursive algorithms, understanding both time and space complexity is essential. Recursive calls can quickly multiply, making it critical to calculate how these calls scale as input grows. Recursive algorithms may require additional memory for each function call, impacting performance if not carefully considered.

In This Chapter, we will cover below methods to find the complexity of recursive algorithms.

  • Recursion Tree Method
  • Recurrence Relation Method
  • Master Theorem

These methods will help us evaluate the efficiency of recursive algorithms, giving us insights into their performance and scalability.

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Table of Contents

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