Github user neggert commented on the issue:
https://github.com/apache/spark/pull/15018
Alright, the PAV algorithm has been completely re-written to follow what's
outlined in "Minimizing Separable Convex Functions Subject to Simple Chain
Constraints". I've tested it with a bunch of different inputs that caused
previous version of this algorithm to go non-polynomial. It stays linear for
all of them. I will note that it's slightly slower on very small datasets (<
5000 points or so), but those still finish in less than a millisecond on my
laptop, so I'm not too concerned.
The only caveat is that there was one test that I couldn't get passing, so
I removed it. It involved passing input data with 0 weights. I'd argue that the
isotonic regression problem doesn't even have a unique solution in that case,
so we shouldn't support it. Still, it is a slight change in behavior.
Looks like Jenkins is pointing out some style issues, so I'll get to work
on those.
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