Earlier quoted context omitted.
I'd agree with you ~1 year ago but I think a lot of their articles now tend to water-down lots of complex subjects in rigorous math papers. Still remains one of my favorite websites.
i think they have to water it down so much because hardly anyone would understand the math, even people who consider themselves good at math or have degrees in STEM..it's just so advanced even relative to that .
Researchers achieve ‘absurdly fast’ algorithm for network flow
71–80 of 80 posts
Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#72Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#73Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#74Here's a talk they gave: https://www.youtube.com/watch?v=KsMtVthpkzI
Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#75Earlier quoted context omitted.
I've been out of school for a while, what does m^(1+o(1)) mean? Isn't any constant trivially in "o(1)"? So m^1000 is in this class? Or are they using it informally to mean "a very small number"?
o(1) is "little O" notation, where for a function y = o(f), y(x) / f(x) --> 0 as x --> infinity. In other words, y goes to 0 faster than f. In this context, m^(1 + o(1)) means roughly that you converge to linear time for large m.
Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#76The best known algorithms, say relabel-to-front, is O(m^1.5) and the paper proposes an algorithm which is O(m^(1+o(1)). Here, 1.5 is fixed but 1+o(1) can be anywhere between 1 and 2 based on your luck, and should be 1.5 on an average over variety of inputs. Still no free lunch.
Uh, no, based on the size of your input. o(1) means something that goes to 0 as m gets bigger, it has nothing to do with randomness.
Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#77The best known algorithms, say relabel-to-front, is O(m^1.5) and the paper proposes an algorithm which is O(m^(1+o(1)). Here, 1.5 is fixed but 1+o(1) can be anywhere between 1 and 2 based on your luck, and should be 1.5 on an average over variety of inputs. Still no free lunch.
> based on your luck Uh, no, based on the size of your input. o(1) means something that goes to 0 as m gets bigger, it has nothing to do with randomness.
Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#78Earlier quoted context omitted.
> based on your luck Uh, no, based on the size of your input. o(1) means something that goes to 0 as m gets bigger, it has nothing to do with randomness.
I deliberately wrote "variety of inputs" to already clarify that a single input is a random sample from its underlying distribution.
Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#79Re: Researchers achieve ‘absurdly fast’ algorithm for network flow
#80Earlier quoted context omitted.
o(1) is "little O" notation, where for a function y = o(f), y(x) / f(x) --> 0 as x --> infinity. In other words, y goes to 0 faster than f. In this context, m^(1 + o(1)) means roughly that you converge to linear time for large m.
I wish we could all collectively agree that using = for the o/O/etc. notations is stupid and stop this. These are sets of functions. f is not equal to the set, it is an element of the set and we already have a well defined notation for that: f ∈ o(...). So use that or use the LateX f \in o(...) if you want to restrict yourself to ASCII. Using = only serves to confuse.