Re: [eigen] cppduals - dual number implementation with Eigen specializations

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Yes, of course.  It's mentioned in the paper..  The dual approach is faster (one multiplication fewer per op) and exact (doesn't rely on +h).  The only place where the complex-step might be better is when a) your function is real, and b) you dont need much accuracy, and c) you have BLAS's complex-complex matrix operations that are faster than Eigen's optimization of the dual-valued matrix ops.

There is/was some performance problem with Eigen's optimization of mat-mult of complex-valued matrices (and by extension, my dual-valued Eiegn matrices).  The real-valued operations are/were very close to OpenBLAS on my machines, but the complex valued ops are quite a bit slower.  I mentioned it on the Eigen chat room once, but I didn't have time to track it down and come up with a proper bug-report.  (sorry, it will happen some day.. in case it hasn't already been fixed.)

On Tue, Dec 3, 2019 at 4:04 PM Ian Bell <ian.h.bell@xxxxxxxxx> wrote:
Have you compared against naive complex step derivatives : ?  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 <tesch1@xxxxxxxxx> 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: , 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.


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