Costa Rica
Belinda Brown, [email protected]
This repository contains implementations of various algorithms in Python and C++, with a focus on understanding their time complexity and order of growth.
Time Complexity is a way to represent the amount of time an algorithm takes in terms of the size of the input to the algorithm. It's usually expressed using Big O notation, which describes the upper bound of the time complexity in the worst-case scenario.
Order of Growth is a simplified representation of the time complexity. It describes how the time of execution grows with the size of the input. For example, an algorithm with an order of growth O(n) will have its execution time grow linearly with the size of the input.