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domcoltdon SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code

Hopefully, the SciPy community can stay open-minded about modern Fortran libraries.

Modern Fortran is quite different from Fortran 77, while being as powerful, if not more.

In addition, there has been a significant community effort on improving and modernising the legacy packages, the ecosystem, and the language itself.

With projects like LFortran (https://lfortran.org/), fpm (https://github.com/fortran-lang/fpm), and stdlib (https://github.com/fortran-lang/stdlib), I believe that Fortran will enjoy prosperity again.

domcoltdon Optimization Without Derivatives: Prima Fortran Version and Inclusion in SciPy

Comment from Reddit:

https://www.reddit.com/r/programming/comments/13k74ro/optimi...

“Finally, a fortran package not written in Fortran77 but one trying to be approachable and maintainable rather than the product of unhappy PhD students spending many months to eke out a little more in an incomprehensible, Oracle-comparable mess.”

domcoltdon Optimization Without Derivatives: Prima Fortran Version and Inclusion in SciPy

Indeed, the author mentions many bugs of the SciPy gradient-free solvers in the readme of the repo of Prima.

domcoltdon Optimization Without Derivatives: Prima Fortran Version and Inclusion in SciPy

The extensive tests made by GitHub actions are simply amazing.

domcoltdon Optimization Without Derivatives: Prima Fortran Version and Inclusion in SciPy

> DFOs tend to have issues past a few dozens variables.

It is interesting to check the results in section 5.1 of the following paper:

https://arxiv.org/pdf/2302.13246.pdf

where these algorithms are compared with finite-difference CG and BFGS included in SciPy.

domcoltdon Optimization Without Derivatives: Prima Fortran Version and Inclusion in SciPy

Agreed. If (approximate) 1st-order information can be obtained in any way (even by *carefully* deployed finite difference), then gradient-based methods should be used. It is wrong to use DFO in such cases.

> DFOs tend to have issues past a few dozens variables.

It seems that the PRIMA git repo https://github.com/libprima/prima shows results on problems of hundreds of variables. Not sure how to interpret the results.

domcoltdon Optimization Without Derivatives: Prima Fortran Version and Inclusion in SciPy

Good coffee is needed to understand Powell’s papers on these algorithms. If you don’t have, section 3 of the following paper is a good place to start with:

https://arxiv.org/pdf/2302.13246.pdf

domcoltdon Optimization Without Derivatives: Prima Fortran Version and Inclusion in SciPy

GitHub repo of the project: https://github.com/libprima/prima