|Re: [eigen] cppduals - dual number implementation with Eigen specializations|
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Have you fixed the problem of short cutting operations in eigen sparse matrix routines; see
On 12/3/19 6:36 AM, Michael Tesch wrote:
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:
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.