Have you compared against naive complex step derivatives : https://sinews.siam.org/Details-Page/differentiation-without-a-difference ? For first derivatives, CSD are trivial to apply, and doesn't require any additional machinations. I think that would serve as a nice benchmark.
On Tue, Dec 3, 2019, 8:36 AM Michael Tesch <tes...@gmail.com> wrote: > Hello, > > I've written (yet another!) Dual Number implementation for automatic > differentiation. It is meant to be used as the value-type in Eigen > matrices, and has templates for vectorization (shockingly) similar to (and > based on) Eigen's complex-type vectorizations. It is quite fast for > first-order forward diff, and imho pretty easy to use. There are also > SSE/SSE3/AVX vectorizations for std::complex<dual< float | double >> types. > > The library is here: https://gitlab.com/tesch1/cppduals , and there's a > small paper in JOSS too: https://doi.org/10.21105/joss.01487 > > I hope this could be useful for someone and would be glad for any > feedback, improvements, etc. > > It would be interesting to compare this approach to others, by hand-wavey > arguments I believe it should ultimately be faster in certain cases. > > Cheers, > Michael > >
