Time complexity of an algorithm defines the amount of time taken by an algorithm to run as a function of the input size. Similarly, the space complexity of an algorithm gives a measure for the amount of space (memory) taken by an algorithm to run for a specific length of the input. These complexity metrics define how much time and space an algorithm takes with an increasing amount of data (inputs on which the algorithm has to work on).
For example, consider the problem of adding two numbers. Here, we will look at each digit pair in the two integers, add them, and then move to the next digit pair. If we had to denote the time taken to perform this addition, we could model it as, T(n) = c * n:
- Here T(n) is the time taken ...
