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>> An O(n) startup grows its key metric (revenue, users, etc.) roughly linearly with time—double the time, double the metric. An O(n^2) startup accelerates, wit
by thaumasiotes 1y ago
>> An O(n) startup grows its key metric (revenue, users, etc.) roughly linearly with time—double the time, double the metric. An O(n^2) startup accelerates, with growth compounding super-linearly over time.
> Kind of a strange formulation to have n represent the key metric. In algorithm analysis, we would typically have n represent time
In the quote you pulled, n is time. If n were the key metric, everything would be ϴ(n).
> So we would say that the startup whose key metrics accelerate exponentially with time is actually an O(log n) startup - they only have to spend (log n) time to get n results.
No, you don't know how the notation is used.
- wavemode 1y ago> In the quote you pulled, n is time. It's definitely not. If their usage of O(n) has n as time, then they wouldn't say an O(n^2) startup has accelerated growth of the key metric. You'd be squaring the time, which means slowing down growth of the key metric. When they say O(n^2) startup they clearly mean a startup which achieves n^2 results in n time. Which is the opposite of how the notation would typically be used. > No, you don't know how the notation is used. No, you're confidently wrong.
- thaumasiotes 1y ago>> In the quote you pulled, n is time. > It's definitely not. > When they say O(n^2) startup they clearly mean a startup which achieves n^2 results in n time. How did you manage to write this down without noticing what you were saying?