Automatic Differentiation is not applicable to symbolics, see:
https://en.wikipedia.org/wiki/Automatic_differentiation

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On Thu, Mar 1, 2018 at 11:16 AM, Aaron Meurer <[email protected]> wrote:

> I haven't heard of tangent before.
>
> Can you clarify more what the project would do? Wouldn't it be enough
> to just run sympy.python() on an expression, then pass that to
> tangent?
>
> Aaron Meurer
>
> On Thu, Mar 1, 2018 at 12:46 PM, Navjot Kukreja <[email protected]> wrote:
> > Hello everyone
> >
> > I am Navjot Kukreja, a PhD student at Imperial College London. I would
> like
> > to propose a GSoC project to enable automatic differentiation of Sympy
> > constructs using tangent (https://github.com/google/tangent). Tangent
> does
> > source-to-source automatic differentiation directly on python code. The
> > ability to perform automatic differentiation on python code that contains
> > sympy constructs would enable/simplify the use of sympy in defining
> various
> > optimization problems, which is a very broad class of problems in
> > HPC/Science/Engineering. It also makes it easier to use sympy in the
> > construction of DSL compilers.
> >
> >
> > Has this been considered before? Is there someone in the community that
> has
> > tried this or needs this?
> >
> >
> > Thank you
> >
> > Navjot Kukreja
> >
> > https://github.com/navjotk
> >
> >
> > PS: I am one of the developers of Devito (https://github.com/opesci/
> devito),
> > a sympy-based DSL for the rapid development of high-performance
> > finite-difference simulations. The primary use of devito is as part of
> > PDE-constrained optimization problems where the problem definition,
> > including the objective function, governing PDE, interpolators etc. are
> > provided as a combination of sympy expressions and pure python.
> >
> >
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