time-complexity-analysis

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.

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.

In DSA, learning the time complexity analysis of recursion is one of the critical steps in mastering problem-solving using recursion. In this blog, we will discuss: 1) How to write recurrence relations of recursive algorithms with various examples 2) Steps to analyze the time complexity of recursion 3) Popular methods of analysis like the recursion tree method and the master theorem.