What are the differences in algorithm efficiencies?

What are the differences in algorithm efficiencies?

Algorithm efficiency refers to how well an algorithm performs in terms of time and space. It can be measured using Big O notation.

Can you explain Big O notation?

Big O notation describes the upper limit of an algorithm's running time or space requirements as the input size grows. For example, O(n) means the time grows linearly with the input size.

What are some common complexities I should know?

Common complexities include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, O(n log n) for linearithmic time, and O(n^2) for quadratic time.

How do I choose the right algorithm for my problem?

Choosing the right algorithm depends on the problem's requirements, such as the size of the input, the need for speed, and memory constraints. Analyze these factors to make an informed choice.