Measuring efficiency and the Big-O notation

Any algorithm is going to have its own running time and space complexity. As we have seen, these two variables are not fixed, and usually they depend on the input data. We have also seen that we can have a high level idea with the best, worst, and average complexities. In order to express them in an easy way, we are going to use asymptotic analysis and the Big-O notation.

Asymptotic analysis

Asymptotic analysis gives us the vocabulary and the common base to measure and compare an algorithm's efficiency and properties. It is widely used among developers to describe the running time and complexity of an algorithm.

Asymptotic analysis helps you to have a high-level picture of how an algorithm behaves in terms of memory and speed depending on the amount of data to process. Look at the following example.

Imagine a very simple algorithm that just prints the numbers of an array one by one:

let array = [1,2,3,4,5] 
for number in array { 
    print(number...