Difference between Big-O and actual execution time
Updated Apr 28, 2026
Short answer
An intermediate dive into Difference between Big-O and actual execution time.
Deep explanation
Algorithms at this level often involve divide-and-conquer strategies or dynamic data structures. For example, O(log n) occurs when the problem size is halved at each step.
Real-world example
Using a phone book to find a name using the middle-split method.
Common mistakes
- Thinking all loops are O(n)
- some decrease the input size exponentially.
Follow-up questions
- Is O(n log n) better than O(n^2)?
- Does recursion always increase space complexity?