5 ms·
Slow I can tell you that! Unless its an open problem that no one is sure on yet. And you get lots of optimizations for a certain class/subtype of that problem.
by anfilt 5y ago
Slow I can tell you that! Unless its an open problem that no one is sure on yet. And you get lots of optimizations for a certain class/subtype of that problem.
Alot ground work has already been laid down. Also most the times when an algorithm with a better time conplexity is found it only applies to really large n. Sometimes that n is so large its basically not even useful for actual computation aka look up the term galactic algorithm.
- DennisP 5y agoFrom the study: > We find enormous heterogeneity in algorithmic progress, with nearly half of algorithm families experiencing virtually no progress, while 14% experienced improvements orders of magnitude larger than hardware improvement (including Moore’s law). Overall, we find that algorithmic progress for the median algorithm family increased substantially but by less than Moore’s law for moderate-sized problems and by more than Moore’s law for big data problems.
- anfilt 5y agoI did look at the paper too so not sure why you need to just re-post an excerpt, and that is basically what I said. Like they said more than half of the families of algorithms they looked at basically are at a stand still.
- DennisP 5y agoI mean, of course you're not going to do much better at sorting, for example. But things like machine learning and optimization are advancing at a rapid pace, and I'm not sure I would characterize them as "open problems that no one is sure on yet." Unless you just want to apply that characterization to anything that's still improving, which would be a bit of a tautology.