There are several loop patterns in programming like a loop running constant time, a loop running n times, a loop growing exponentially, a loop running based on the specific condition, consecutive single loops, two nested loops, three nested loops, consecutive single loop with nested loops, etc. So for designing a better algorithm or optimizing the code, we should learn to analyze the time complexity of the loop in terms of Big-O notation.
Learning analysis of recursion is critical to understand the time complexity analysis of recursive algorithms. We will discuss these concepts related to the recursion analysis: Recurrence relations of recursive algorithms, steps to analyze the time complexity of recursion, Recursion tree method, and master theorem to analyze divide and conquer algorithms.
Explore this blog to answer these questions related to complexity analysis: why time complexity analysis is important? What are the criteria to define the complexity of an algorithm? How do we analyze the time complexity of an algorithm? How do we represent algorithm time complexity in the form of big O notation?
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