MEPX
Chapter 12 of 12All chapters
  1. 1Why complexity matters
  2. 2Reading Big O
  3. 3Constant and linear time
  4. 4Logarithmic time
  5. 5Sorting and its floor
  6. 6Quadratic and worse
  7. 7Best, average and worst
  8. 8Space complexity
  9. 9Amortised analysis
  10. 10Costs by data structure
  11. 11Recursion and recurrences
  12. 12Choosing an algorithm

Chapter 12 of 12

Choosing an algorithm

When the notation is not the answer.

Small n changes everything

A tight O(n squared) beats a loose O(n log n) on small inputs, which is why real sort implementations switch to insertion sort under a few dozen elements.

  • Ask what n actually is in production before optimising the shape.
  • Cache behaviour and allocation often dominate at small sizes.

Simplicity counts

The fastest correct code nobody understands is a liability. Reach for the clearer algorithm until measurement says otherwise, then leave a comment explaining why the clever one is there.