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I tested bybobyqa e.g. with the rastrigin function for different input dimensions.
The numerical results were very good but I got an increasingly strong speed penalty.
For 50 dimensions it was already very noticeably.
I also tested the BOBYQA implementation of apache.commons.math (Java) which does not seem to have this issue (but other issues).
Probably a profiler could easily pinpoint the reason.
The text was updated successfully, but these errors were encountered:
This implementation doesn't include all of the original code's linear algebra tricks which help it scale, so the per-iteration cost is roughly cubic with the input dimension (if you use the default npt=2n+1, worse if objfun_has_noise=True). Improving scalability is an ongoing project for me, but in the meantime you could try reducing npt.
Also, on my machine, I sometimes have an issue with NumPy calling LAPACK, and LAPACK unnecessarily using too many cores, making it much slower than it should be. Setting the environment variables export OPENBLAS_NUM_THREADS=1 and export NUMEXPR_NUM_THREADS=1 helps a lot in this case.
I tested bybobyqa e.g. with the rastrigin function for different input dimensions.
The numerical results were very good but I got an increasingly strong speed penalty.
For 50 dimensions it was already very noticeably.
I also tested the BOBYQA implementation of apache.commons.math (Java) which does not seem to have this issue (but other issues).
Probably a profiler could easily pinpoint the reason.
The text was updated successfully, but these errors were encountered: