|[eigen] Eigen-AD: Algorithmic Differentiation of the Eigen Library|
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We would like to draw your attention to a new fork of Eigen with support
for Algorithmic Differentiation (AD) in the context of AD by overloading
in C++. Important new features include highly efficient first and higher
derivatives of Eigen solvers as well as compatibility with a wide range
of AD software tools. See
for details including benchmark results. Please contact
info@xxxxxxxxxxxxxxxxxxx for access to the software.
We look forward to further discussions with potential users and other AD
software tool developers.
With kind regards
Patrick Peltzer, Johannes Lotz, Uwe Naumann
Informatik 12 (STCE), RWTH Aachen University, Germany