We have explained these concepts related to complexity analysis in data structures and algorithms: 1) What is time complexity? 2) Why time complexity analysis important? 3) Assumptions for performing analysis of algorithms 4) Steps to analyze time complexity 5) How do we calculate algorithm time complexity in terms of big-O notation? Etc.
There are several for and while loop patterns in programming: loop running constant or linear time, loop growing exponentially, loop running on a specific condition, two nested loops, three nested loops, etc. So to design an efficient algorithm and optimize code further, we should learn to analyze time complexity of loop in terms of big-O notation.
Learning analysis of recursion is critical to understand time complexity analysis of recursive algorithms. We will discuss these concepts of recursion analysis: recurrence relations of recursive algorithms, steps to analyze time complexity of recursion, recursion tree method, master theorem to analyze divide and conquer algorithms, etc.
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