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In case you appreciate LFortran and would like to sponsor its development, see

- Become a sponsor to LFortran: https://github.com/sponsors/lfortran

- Open Collective: https://opencollective.com/lfortran

- NumFOCUS: https://numfocus.org/donate-to-lfortran


For more information about LFortran, see

- Wikipedia: https://en.wikipedia.org/wiki/LFortran

- Homepage: https://lfortran.org

- GitHub: https://github.com/lfortran/lfortran

If you like to play with it, see

- LFortran in web browsers using WebAssembly: https://dev.lfortran.org

- LFortran in Compiler Explorer: https://godbolt.org/z/EfWvsY6Kh



No, it is not difficult. It is way more than that. It is frustrating and depressing according to my experience attempting to modernize a FORTRAN 77 codebase (I gave up finally). Navigating through a quagmire of GOTO statements felt like a desperate endeavor. It's hard to fathom how someone could work on such a project for three years, especially if it appears to be a solo effort.

Upon examining the original FORTRAN code and accompanying papers, I realized that the complexity of the task was far from trivial. Working alone for three years, especially on such a complex codebase, requires a significant amount of dedication and perseverance.


These people https://arxiv.org/pdf/1307.5867.pdf use Powell's derivative-free optimization method (newuoa) in the study of the galaxy cluster MACS J1206.2-0847 ( https://it.wikipedia.org/wiki/MACS_J1206.2-0847 ). This is so cool!


“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.


This is truly impressive! While I'm not well-versed in these specific techniques, I do recall learning about Powell during a mathematics course in the past. It's reassuring to know that capable individuals are maintaining his work.

Some time ago, I attempted to update an antiquated FORTRAN program for a particular project, but regrettably, I had to abandon the effort. Navigating the labyrinth of countless GOTO statements was both challenging and disheartening. I became genuinely concerned about the potential impact on my mental well-being.

That being said, I have a profound admiration for the individual who successfully modernized such a complex FORTRAN project. Their achievement is undoubtedly commendable!


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