pycppad-20140710: A Python Algorithm Derivative Package
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from pycppad import *

The command above imports a boost::python interface to the C++ Algorithmic Differentiation (AD) package CppAD .

Simple Example
from pycppad import *
def pycppad_test_get_started() :
  def F(x) :                                   # function to be differentiated
    return exp(-(x[0]**2. + x[1]**2.) / 2.)    # is Gaussian density
  x     = numpy.array( [ 1.,  2.] )
  a_x   = independent(x)
  a_y   = numpy.array( [ F(a_x) ] ) 
  f     = adfun(a_x, a_y)
  J     = f.jacobian(x)                        # J = F'(x)
  assert abs( J[0, 0] + F(x) * x[0] ) < 1e-10  # J[0,0] ~= - F(x) * x[0]
  assert abs( J[0, 1] + F(x) * x[1] ) < 1e-10  # J[0,1] ~= - F(x) * x[1]

Operator overloading is used to store the operation sequence corresponding to a python algorithm. The operation sequence can be evaluated to obtain new function values or derivatives of arbitrary order. In addition, multiple levels of AD are supported. This means that AD derivatives can be used in the definition of a function which in turn can be differentiated using AD. See whats_new for a list of recent extensions and bug fixes.

_contentsTable of Contents
installInstalling pycppad
get_started.pyget_started: Example and Test
exampleList of All the pycppad Examples
ad_variableAD Variable Methods
ad_functionAD Function Methods
two_levels.pyUsing Two Levels of AD: Example and Test
runge_kutta_4Fourth Order Runge Kutta
whats_newExtensions, Bug Fixes, and Changes
_referenceAlphabetic Listing of Cross Reference Tags
_indexKeyword Index
_searchSearch Python Algorithmic Differentiation Using CppAD
_externalExternal Internet References

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