What is the difference between Big O notation and actual time complexity?

What is the difference between Big O notation and actual time complexity?

Big O notation describes the upper limit of an algorithm's running time as the input size grows, while actual time complexity measures the real time taken by an algorithm to run on specific inputs.

Can you give me an example of both?

Sure! For example, a sorting algorithm like QuickSort has a Big O notation of O(n log n) in the average case, but the actual time can vary based on the input data and system performance.

Why is Big O notation important?

Big O notation is important because it helps developers understand the efficiency of algorithms, allowing them to make informed choices about which algorithm to use based on expected input sizes.

How do I calculate Big O for my algorithm?

To calculate Big O, analyze the algorithm's loops and recursive calls, focusing on the most significant factors that affect performance as input size increases.