Can you explain what Big O notation means and how it is used to evaluate algorithms?

The Big O notation is used to estimate the asymptotic complexity of algorithms, that is, how the execution time or the amount of memory used grows with increasing input size.

It describes the upper bound of the growth of a function, ignoring constants and less significant terms. For example:

• O(1) — constant time, independent of data size.
• O(n) — linear time, grows proportionally to the input size.
• O(n²) — quadratic time, grows proportionally to the square of the input size.

Example: if you have a list of n elements and want to find an element by brute force, it will be O(n), since in the worst case, you will need to check all elements.

Using the notation helps compare algorithms and choose the most efficient ones for large data volumes.