Time Complexity and Big O Notation Quiz

The analogy used in the text describes the scenario of wanting to get pizza, where the distance to the pizza place and the quantity of pizza needed represent the input size, and the ability of the brother to handle the request represents the algorithm's efficiency in handling the input size. It illustrates how the algorithm's efficiency (brother's ability to handle the pizza request) can vary based on the input size (distance to pizza place and quantity of pizza needed), similar to how time complexity measures the efficiency of algorithms in processing different input sizes.

Time complexity is the study of the efficiency of algorithms. It measures how much time is taken by an algorithm to process a given input, providing insights into the algorithm's performance as the input size increases.

For 10 elements, Shubham's algorithm took 90 ms, while Rohan's algorithm took 122 ms. For 70 elements, Shubham's algorithm took 110 ms, while Rohan's algorithm took 124 ms. The results indicate that Shubham's algorithm was more efficient for smaller input sizes, but Rohan's algorithm became relatively more efficient as the input size increased.

The analogy of pizza delivery relates to algorithm time complexity by illustrating how the ability of the brother to handle the pizza request mirrors an algorithm's efficiency in handling different input sizes. Just as the brother's ability to handle the pizza request varied based on the input size, algorithms exhibit different levels of efficiency when processing different input sizes, as indicated by their time complexity.

Comparing the time taken by Shubham's and Rohan's algorithms for different input sizes provides insights into how their efficiency varies as the input size increases. This comparison helps in understanding the performance of the algorithms and their suitability for different input sizes, contributing to the study of time complexity and algorithm efficiency.