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 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). Time Complexity is the amount of time that any algorithm takes to function . It is basically the function of the length of the input to the algorithm. Time Complexity measures the total time it takes for each statement of code in an algorithm to be executed. Time complexity is defined as the amount of time taken by an algorithm to run, as a function of the length of the input. It measures the time taken to execute each statement of code in an algorithm. It is not going to examine the total execution time of an algorithm.