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Alphabetical List of Tools

• ad  (Python)
  Transparent, calculator-style first and second-order derivatives.
  
  
• ADOL-C  (C/C++,Julia,Python)
  The package ADOL-C facilitates the evaluation of first and higher derivatives of vector functions that are defined by computer programs written in C or C++. The resulting derivative evaluation routines may be called from C/C++, Fortran, or any other language that can be linked with C. ADOL-C is distributed by the COIN-OR Foundation with the Common Public License CPL or the GNU General Public License GPL.
  
  
• AuDi  (C/C++,Python)
  AuDI is an open source, header only, C++ library that allows for AUtomated DIfferentiation implementing a Taylor truncated polynomial algebra (aka differential algebra). Its core is also exposed as a python module called pyaudi.
  
  
• CasADi  (C/C++,MATLAB,Python)
  CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs.
  
  
• cppad_py  (Python)
  A C++ Object Library and Python Interface to CppAD.
  
  
• JAX  (Python)
  JAX is Autograd and XLA, brought together for high-performance machine learning research.
  
  
• MatLogica AADC  (C#,C/C++,Python)
  MatLogica AADC approach uses Code Transformation and Operator Overloading to efficiently compute the gradients of mathematical models by generating optimized machine code at runtime. This results in faster computing of the model and its first and higher order derivatives. The approach is particularly useful for models with many parameters that require frequent gradient updates during training.
  
  
• pyadolc  (Python)
  Python Wrapper of ADOL-C
  
  
• pycppad  (Interpreted,Python)
  A boost ::python interface to the C++ Algorithmic Differentiation package CppAD. The pycppad package is distributed under the BSD license.