Lesson 062
Algorithm Efficiency
Big O Notation
1:00How to measure and compare algorithms by how their operation count grows with input size, using Big O notation and the five common complexity classes.
By the end, you can
- Explain why operation counts, not wall-clock seconds, define algorithmic efficiency.
- Rank the five common complexity classes from slowest-growing to fastest-growing.
- Count the operations in a simple loop or nested loop and identify its Big O class.
- Describe what O(1), O(log n), O(n), O(n log n), and O(n²) each mean in plain terms.
- Apply the two simplification rules to reduce a raw operation count to its Big O class.
- Distinguish best-case, worst-case, and average-case complexity and explain which one Big O typically states.
- Explain that Big O measures both time and space complexity, and that one can be traded for the other.
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