ggraphilia
China blew up its own future [video]
Vibe physics: The AI grad student
Compression is all you need: Modeling Mathematics
Humanoid Half-Marathon
The weirdest paradox in statistics (and machine learning) [video]
The most beautiful formula not enough people understand [video]
https://fortran-lang.discourse.group/t/lfortran-compiles-pri...
SciPy builds for Python 3.12 on Windows are a miracle
https://labs.quansight.org/blog/building-scipy-with-flang
which was once discussed on HN at
Supporting BFLOAT16 in Fortran: "Not Recommended"?
[5] D. M. Bates, M. Mächler, B. Bolker, and S. Walker. Fitting linear mixed-effects models using lme4. J. Stat. Softw., 67:1–48, 2015.
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According to the papers metnioned by others, the algos under discussion do not seem to support this point. The author of the project mentiones that the FORTRAN 77 implementation is `nontrivial to understand', which motivates this work.
`Caveat: derivative free optimizers are terrible for constrained optimization however (especially if your constraints are anything but simple bounds). You can use barrier methods but still they’re not great.'
Lincoa and cobyla in the package can handle problems with nontrivial constriants according to the documentation of the git repository, though I do not know how well they work.