Finally got time to read the article and got the sense of deja vu, but later remembered that it is your report from kotlinconf. We actually have this specific problem (in practice, not as a toy example) at general physic department at MIPT, but we have much more complicated set of constrains (each lecturer has his own preference about days, grouping of lectures and sometimes specific student groups). In our case we do not need perfect solution and most of constraints a "soft" rather than "hard" meaning that some preferences could be ignored. So I was thinking about some kind of bayesian solution. Sadly, I have my hands full with new laboratory and all this math library stuff, so I don't know where will I have time for that.

@altavir I don't think that sounds like a Bayesian problem at all. It is more than possible to juggle hard and soft contraints, and pursue feasibility vs "nice to have" objectives using metaheuristics or tree search. I'm doing a project at work with this nature. If you haven't done discrete optimization for really complex problems, I think you could rip through this class in 6 weeks if you needed to. https://www.coursera.org/learn/discrete-optimization

Sadly, do not have time for it right now. Maybe I will ditch it on one of my phd-students who actually work with classic network-ML.

@altavir just don't sleep and you'll find time :trollface:

I am OK with that. My wife is not. And one just does not argue with my wife. ☠️