For what purpose is Big O notation used?

Big O notation is primarily used to classify algorithms based on their run time or space requirements as the input size grows. This notation provides a high-level understanding of the efficiency of an algorithm, enabling developers and computer scientists to compare the complexities of different algorithms regardless of the specifics of implementation or the hardware used.

By using Big O notation, one can express the upper limit of an algorithm’s growth rate, which is crucial when analyzing how the performance of an algorithm will scale with larger inputs. This classification allows for easier communication and understanding of algorithm efficiency, as it abstracts away the lower-order terms and constants that become less significant in comparison to the dominating term as the input size increases.

Therefore, the purpose of Big O notation is fundamentally about assessing and comparing the algorithmic performance in terms of time and space complexity, making it an essential tool in computer science and algorithm design.