Google Summer of Code Student 2020
email: r.intval@gmail.com
Education: Mathematics and Computer Science, Voronezh State University, Russia
project:
Extend clad to compute Jacobians
In mathematics and computer algebra, automatic differentiation (AD) is a
set of techniques to numerically evaluate the derivative of a function
specified by a computer program. Automatic differentiation is an
alternative technique to Symbolic differentiation and Numerical
differentiation (the method of finite differences). CLAD is based on
Clang which will provide the necessary facilities for code
transformation. The AD library is able to differentiate non-trivial
functions, to find a partial derivative for trivial cases and has good
unit test coverage. Currently, clad does not provide an easy way to
compute Jacobians.
Project Proposal: URL
Project Reports: Poster
Mentors: Vassil Vassilev, Alexander Penev
We are part of the
Princeton Department of Physics
and
Princeton Institute for Computational Science and Engineering
(PICSciE).
Contact: David Lange; Vassil Vassilev
This project is supported by the National Science Foundation under Grant OAC-1931408 (2020-2023) and OAC-2311471(2023-2026). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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