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, Matlab/Octave, Julia or Javascript

javascript python c-plus-plus library optimization matlab mathematics nonlinear octave numerical-calculations scientific-computing derivatives code-generation parameter-estimation academic-project optimal-control symbolic-manipulation julia-package algorithmic-differentation nonlinear-programming

• Updated Nov 11, 2026
• C++

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

machine-learning statistics neural-network optimization matrix linear-algebra automatic-differentiation regression autograd numerical-calculations scientific-computing topic-modeling ndarray plotting gsl maths sparse-linear-systems statistical-functions mcmc algorithmic-differentation

• Updated Oct 17, 2026
• OCaml

Source Code Generation for Automatic Differentiation using Operator Overloading

c-plus-plus latex automatic-differentiation jit mathml dae differentiation algorithmic-differentation

• Updated May 27, 2026
• C++

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

library cpp automatic-differentiation header-only algorithmic-differentation

• Updated Nov 29, 2022
• C++

Complex Numbers for Algorithmic Differentiation

automatic-differentiation complex-numbers algorithmic-differentation

• Updated Feb 5, 2018
• C++

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

cuda algorithmic-differentation forward-mode subgradients

• Updated Nov 9, 2026
• Cuda