Can you explain the concept of algorithm complexity assessment using Big O notation?

Big O notation is used to estimate the complexity of algorithms, showing how the execution time or memory consumption grows depending on the size of the input data.

Key points:

• Big O describes the upper bound of the growth of a function, ignoring constants and less significant terms.
• For example, an algorithm with a complexity of O(n) will take roughly twice as long if the size of the input data doubles.
• Different complexities include: O(1) — constant time, O(log n) — logarithmic, O(n) — linear, O(n²) — quadratic, etc.

Example: if you have a loop that goes through all elements of an array of n elements, the complexity is O(n). If there is another nested loop inside this loop over the same n elements, the complexity becomes O(n²).

This helps in choosing more efficient algorithms and evaluating code performance.