Time complexity: In the example above

In the example above, there is a nested loop, meaning that the time complexity is quadratic with the order O (n^2). Exponential Time : O (2^n) You get exponential time complexity when the growth rate doubles with each addition to the input (n), often iterating through all subsets of the input elements. Learn what time complexity is, its types, and examples. Understand how it impacts algorithm efficiency and problem-solving in computing. These two concepts help us measure how much time and memory an algorithm uses as the input grows. Let’s break them down in simple terms. ⚡ 1. What is Time Complexity ? Time complexity tells us how much time an algorithm will take to run as the input size increases. Learn how to evaluate and compare the runtime of algorithms using time complexity , Big O notation, and worst, best and average case scenarios. See examples of different algorithms and their time complexities, such as O(1), O(n), O(nlogn) and O(n2).