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>And sure that wouldn’t work with arbitrary Cython modules because Cython was designed as a Pythonic syntax over the Python C-API, and it just happened to becom

>And sure that wouldn’t work with arbitrary Cython modules because Cython was designed as a Pythonic syntax over the Python C-API, and it just happened to become popular for numerical work. >I don’t think that’s a strong argument, though, because anything small enough to be usable with AD can be rewritten without too much time lost, whereas those massive Fortran routines with iterative algorithms wouldn’t produce useful gradients in any language. No. In Julia you can just stick the entire delay differential equation solver into the AD functions and get a gradient for parameter estimation. Saying you cannot use an arbitrary Cython code is a limitation, and saying you cannot put a random large code into AD is a limitation. It wouldn't be an issue if these weren't already solved problems, but having a performant software with simple and available AD is not something that's unreasonable anymore. If you use Julia, it's just something you can expect to work.

marmaduke 8y ago
No you can’t, at least, not if you want it work. This has nothing to do with Julia though and I didn’t say you can’t run AD over the solver but that that generally will not produce good gradient estimates unless the solver is written with AD in mind. DDEs are a mixed case where I’d expect some parts to be workable but not in general. Another example is the FFT, totally worthless to use AD.