Rank the common Big-O complexities from best to worst.

Difficulty: 2/10

time complexity, algorithm analysis, performance tradeoffs

Big-O Ranking

For sufficiently large input sizes, common complexities can generally be ranked from most scalable to least scalable as O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ), and O(n!).

1
1. O(1) — Constant
2
1. O(log n) — Logarithmic
3
1. O(n) — Linear
4
1. O(n log n) — Linearithmic
5
1. O(n²) — Quadratic
6
1. O(2ⁿ) — Exponential
7

This ranking describes asymptotic scalability rather than actual runtime for small datasets. Constants, hardware, implementation details, cache behavior, and I/O can make a theoretically slower algorithm faster for small inputs.

Scenario Questions

0-2 years experience

Follow-up Questions