algorithmic-differentation

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

Owl - OCaml Scientific Computing @ https://ocaml.xyz

Source Code Generation for Automatic Differentiation using Operator Overloading

A single header-only C++ library for automatic / algorithmic differentiation.

Complex Numbers for Algorithmic Differentiation

CUDA subgradients using forward-mode Algorithmic Differentiation (AD).