Big-O: how running time grows with the input
This animation plots O(1), O(log n), O(n), O(n log n), O(n^2), and O(2^n) on shared axes, showing how each curve grows as input size increases. Each complexity class is paired with a familiar algorithm example, such as array lookup, binary search, linear scan, sorting, nested loops, or recursive subsets. It helps students see why constants are ignored and why shape, not exact values, determines scalability, making it useful for computer science and algorithms courses.
16:9 · every frame verified for overlaps, spacing and edges before rendering
The prompt that made it
Curves for O(1), O(log n), O(n), O(n log n), O(n^2) and O(2^n) drawn on the same axes with an example algorithm for each; constants are dropped, the shape is what matters.
Make the next one in this series
- Zoom into small n to show why constants can matter before asymptotic behavior dominates
- Animate a sorting algorithm's actual operation count against its O(n log n) curve
- Compare best case, average case, and worst case complexity for quicksort on one graph
