In the previous article, Measuring the performance of algorithms, we measured the time it took to execute the algorithms bubble sort and get_max_value. That comparison allowed us to see how \(O(n^2)\) algorithms perform compared to an \(O(n)\) algorithm in practice. We saw a big difference

In our previous article, Big-O notation and complexity theory, we saw how an algorithm categorized as \(O(n)\) is more efficient than one classified with a complexity of \( O(n^2)\). Still, we only compared the algorithms in theory. In this article, we will see how well the theory matches

Big O notation and complexity theory are fundamental concepts in computer science that students typically learn about these topics when they take an algorithms class. Outside academics, Big O notation and complexity theory are mostly buzzwords and occasionally appear during a job interview. In my experience, most people tend to