cppad_py#

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A C++ Object Library and Python Interface to CppAD#

Version#

cppad_py-2024.4.10

Git Repository#

https://github.com/bradbell/cppad_py

Purpose#

  1. Provide a connection from Python to the Algorithmic Differentiation (AD) package CppAD; see py_lib.

  2. Provide an AD object library; see cpp_lib.

  3. Provide a concrete example of how swig can be used to connect any scripting language to CppAD.

Getting Started#

After you install cppad_py, the following example is a good place to get started using it: Python: Dense Jacobian Using AD: Example and Test.

Numerical Examples#

The following is a link to some numerical examples: numeric_xam.

C++ Function Speed#

One can use CppAD Py to get faster function evaluation in scripting Python, when the sequence of floating point operations does not depend on the independent variables. Once an py_fun is recorded, zero order forward_mode can be used to effectively evaluate the function in C++ instead of Python.

License#

This program is distributed under the terms of the GNU General Public License version 3.0 or later see gpl-3.0.txt.

Children#

Name

Title

install

Installing cppad_py

numeric_xam

Numerical Examples

library

The CppAD Py Libraries

release_notes

CppAD Py Release Notes