How do I compare the time complexity of different algorithms?

How do I compare the time complexity of different algorithms?

To compare time complexity, you analyze the growth of the algorithm's running time as the input size increases. Common notations include Big O, Big Θ, and Big Ω.

Can you give me examples of algorithms with different time complexities?

Sure! For example, a linear search has a time complexity of O(n), while a binary search has O(log n). Quick sort has an average time complexity of O(n log n).

What does O(n log n) mean in practical terms?

O(n log n) means that as the input size n increases, the time taken grows proportionally to n times the logarithm of n. It's efficient for large datasets.

How do I determine the time complexity of my own algorithm?

You can determine time complexity by analyzing the algorithm's loops, recursive calls, and the operations performed within them. Count the number of basic operations as a function of input size.