Programming Pointers that Helped Me With Technical Interview

Common time complexities: O(1), O(log n), O(n), O(n log n), and O(n²)

Time Complexities

O(1) → constant time (already implied in your space section, but worth listing explicitly for time too).
O(n²), O(n³), … → polynomial time in general (you have n² but not n³+).
O(k·n) or O(n + m) → “linear in combined size” is common in graph problems (n = vertices, m = edges).
O(√n) → often appears in number theory, grid traversal optimizations, or block decomposition.
O(n!) → factorial growth, classic in brute force permutation problems.
O(nᶜ) for arbitrary constants c > 1 (general polynomial).
O(n^n) → extremely rare, but worth knowing for completeness.
O(log n) is when size is reduced by a constant fraction each step, like binary search
O(n log n) is when it is turned into a subproblem that is solved independently, like merge sort or when there is an O(n) part and recursion part

Space complexities:

O(1) uses a constant amount of extra space
O(log n) typically for recursion, it is space proportional to the logarithm of the input
O(n) the space proportional to the input size
O(n log n) certain divide and conquer algos that use extra data structures
O(n²) uses the square of the input size