For PRIMA, see

https://github.com/libprima/prima

https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

Discussions on the Fortran Discourse:

https://fortran-lang.discourse.group/t/lfortran-compiles-pri...

It may not be that simple on Windows : https://labs.quansight.org/blog/building-scipy-with-flang

Which major (applied) math/statistics journal accept submissions in typst？

You're right, people submitting for academic publications will still need to use LaTeX until those institutions change their practices.

If that group comprises the vast majority of people who might have a use for a programmatic typesetting environment, and if the use of LaTeX by academic institutions represents current, expert insight about LaTeX's continued superiority and not simply organizational inertia, then Typst is irrelevant and pointless.

Big "if"s, though.

Long term user of LaTeX. I did try Typst and it has is advantages main one it compiles faster. I am sticking with LaTeX and I don't find it difficult to use, write packages and classes, as I did invest the time to understand it and learn the language. Academic institutions, understand its superiority and also want to protect their archives. Maths has a long shelf-life. LaTeX also has a very good civilized community. LaTeX as it stands now has no comparative competitor.

See

https://labs.quansight.org/blog/building-scipy-with-flang

which was once discussed on HN at

https://news.ycombinator.com/item?id=38196412

That's not the same compiler.

Right. The article talks about building SciPy using LLVM-Flang, although it does mention LFortran a few times as well.

Also https://www.mat.uc.pt/~lnv/papers/mjdp.pdf

How surprising that they release a product that contains so many bugs. Very impressive that all of them come with a small an nice reproducing example.

“Renowned for their robustness and efficiency, these solvers are used in a wide spectrum of applications, for instance, aeronautical engineering [25], astronomy [39], computer vision [33], robotics [40], and statistics [5].” These algorithms seem truly used in science and engineering.

[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.

[25] F. Gallard et al. GEMS: a Python library for automation of multidisciplinary design optimization process generation. In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Kissimmee, FL, USA, 2018. AIAA.

[33] H. Izadinia, Q. Shan, and S. M. Seitz. IM2CAD. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5134–5143, San Juan, PR, USA, 2017. IEEE.

[40] K. Mombaur, A. Truong, and J. P. Laumond. From human to humanoid locomotion—an inverse optimal control approach. Auton. Robot., 28:369–383, 2010.

[39] G. A. Mamon, A. Biviano, and G. Boué. MAMPOSSt: modelling anisotropy and mass profiles of observed spherical systems I. Gaussian 3D velocities. Mon. Not. R. Astron. Soc., 429:3079–3098, 2013.

LFortran sounds to be a genenius project. The interactivity is a critical block that is missing from FORTRAN ecosystem. Hope things will get better with LFortran!

`they are also much simpler to implement ...'

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.