What does the Big O notation mean in algorithm analysis?

The Big O notation is used to describe the asymptotic estimate of an algorithm's complexity, that is, how the execution time or memory consumption grows depending on the size of the input data.

Big O shows the upper bound of the growth of an algorithm's resource consumption as the data volume increases, ignoring constants and less significant terms.

For example:

• O(1) — constant time, independent of data size.
• O(n) — linear time, proportional to the input size.
• O(n²) — quadratic time, such as nested loops.

Example in Python:

# Linear search - O(n)
def linear_search(arr, target):
    for i in arr:
        if i == target:
            return True
    return False

Big O helps compare the efficiency of algorithms and choose optimal solutions.