This toolbox provides forward automatic differentiation. It is based on operators and functions overloading.
Given a Scilab code computing a variable y depending on a variable x and a direction dx it allow evaluation of y together with the directional derivative Grad(y)*dx.
The module supports common arithmetic operations, common elementary functions and several matrix functions, including matrix inversion.
for a quick start.
The following is a list of the current functions :
- diffcode_der - Create a new code differentiation object.
- diffcode_CDcost - Objective function for optim.
- diffcode_hessian — Compute the Hessian of the function.
- diffcode_jacobian — Compute the Jacobian of the function.
- This module depends on the helptbx module.
- This module depends on the assert module.
- This module depends on the apifun module.
This toolbox was first released at:
in August 2002.
Michael Baudin updated the module for Scilab v5.
- Copyright (C) 2011 - DIGITEO - Michael Baudin
- Copyright (C) 2002 - INRIA - Xavier Jonsson
- Copyright (C) 2002-2009 - INRIA - Serge Steer
This toolbox is released under the CeCILL_V2 licence :