What are the common algorithm complexities I should know about?

What are the common algorithm complexities I should know about?

Common algorithm complexities include O(1), O(n), O(log n), O(n^2), and O(2^n). They describe how the runtime or space requirements grow with input size.

Can you explain what O(n) means?

O(n) means that the time or space complexity grows linearly with the size of the input. If you double the input size, the time taken will also roughly double.

What about O(log n)? How is it different?

O(log n) indicates that the complexity grows logarithmically, meaning it increases much slower than linear. This is typical in algorithms that divide the problem size in each step, like binary search.

Why is understanding these complexities important?

Understanding algorithm complexities helps in choosing the right algorithm for a problem, optimizing performance, and predicting how an algorithm will scale with larger inputs.