3 ms·
the O(n^2) thing sounds reasonable until i start to think about it. but then i get confused. that's some kind of cost, but my naive interpretation (number of
by andrewcooke 14y ago
the O(n^2) thing sounds reasonable until i start to think about it. but then i get confused. that's some kind of cost, but my naive interpretation (number of partners scales with applicants, so total interactions is n^2) spreads that cost across O(n) partners, so the cost per partner is O(n).
what am i missing? am i just being too literal, or have you identified some real process that is O(n^2) per person, or is the total cost more important than i think? or am i just being dumb?
or maybe even handling O(n) per partner is too much. i guess that may be all it is. my initial reading was that O(n^2) explained why 66 and 84 were so different (when linearly, they're pretty similar).
- zissou 14y agoYou're not being dumb. You just sound similar to, say, a graduate student doing theory who hasn't been outside in a while. :)
- deleted 14y ago[deleted]
- peripitea 14y agoThe way I read it, he's referring to communication channels needed for everyone to be on the same page (see Metcalfe's law, for example: http://en.wikipedia.org/wiki/Metcalfes_law http://en.wikipedia.org/wiki/Metcalfes_law). Traditionally, with a homogeneous group of people, the count of communication channels will be O(n^2), as you can see in the images at the top of the wikipedia article, because everyone needs to talk to everyone else. In this case, it's not homogeneous; there are n startups and m partners, but if every partner needs to know what's going on with every startup, you still have O(mn) communication channels. If, for example, you assigned n/m startups to each partner, the total communication channels needed would reduce to O(m+n). I believe pg is referring to something along those lines when he says he'd like to make it unnecessary for each partner to know what each startup is doing.