Closed mayork closed 7 years ago
solve stability seems to decrease as Nmission is increased...are we pushing the bounds of Mosek?
It is very possible. I was reading this from a Mosek blog, and it seems that the NEAR_OPTIMAL is an indicator that the solver is stalling/not making meaningful progress towards a solution. And we start getting NEAR_OPTIMAL's even with 1700 variables...
It might be worth considering doing some variable normalizations to make everything of roughly the same order of magnitude
I think good initial guesses are probably more if not just as important, especially since the near_optimals occur (at least for me) near the beginning of a solve. Is there a way to nicely convert a solution to an x0?
pass in x0=sol
But for the multimission, not for the single one... :P
have to iterate over the single mission solution and turn everything that is vetorized into a vector then store it all in a dict and pass that in for x0
Btw, the multimission runs fine now, but I'm getting that all of the weights are the same for the aircraft for different n_pax. Is it possible something isn't vectorized properly?
by all the weights, what do you mean specifically. all that should be changing is the weight from passengers and fuel burn
and it doesn't run fine if you increase Nmission to 4 it gets sketchy
Since we have resolved the NEAR_OPTIMAL issue, I think the multimission is much more well-behaved. Closing this. Instead, we should focus on issue #82, which still messes with my head.
Could we be pushing the bounds of Mosek? The problem is 5,299 variables