What information entered the process?
Labs / Computer Science
Big-O Growth Grapher
Every algorithm has a growth rate. Drag the marker left and right to pick an input size n, and read off how many operations each complexity class needs. Toggle curves on and off, or flip the y-axis to a log scale to see slow and fast growth side by side.
Labs / Computer Science / Big-O Growth Grapher
Big-O Growth Grapher
Every algorithm has a growth rate. Drag the marker left and right to pick an input size n, and read off how many operations each complexity class needs. Toggle curves on and off, or flip the y-axis to a log scale to see slow and fast growth side by side.
Why it matters
Why explore this?
Computer science turns clear rules into reliable processes. This model lets you step through those rules and see how each decision changes the result.
Explore
Try the model
n 8n² 642ⁿ 2562ⁿ÷n² ×4
Investigate
Questions to test
Predict first, try it in the model, then use what you observe to answer.
What to try
- At what value of n does O(n²) finally overtake O(n log n) — and does it ever look back?
- Switch to a log y-axis. Why do these curving lines suddenly become nearly straight, and what does a steeper line mean?
- Turn off everything except O(2ⁿ). How far can you push n before the count leaves the top of the chart entirely?
- Compare O(1) and O(log n) at n = 256. Is "logarithmic" really that far from "constant"?
- If a faster computer does 10× more operations per second, which curve does that speed-up help the least?
Explain
Make sense of what happened
Trace the process one step at a time. Name the rule used at each important step, then explain why the final result follows from those rules.
Check
Before you leave
Which rule changed it?
Why did the process produce this result?
If you can answer all three using evidence from the model, you understand the main idea. If not, return to Explore and change one setting at a time.