Prev

Next

Index-> contents reference index search external

Up-> cppad_py library cpp_lib cpp_fun cpp_fun_jacobian

cppad_py-> setup.py library whats_new_2018

library-> py_lib cpp_lib

cpp_lib-> a_double vector cpp_fun sparse cpp_utility more_cpp

cpp_fun-> cpp_independent cpp_abort_recording cpp_fun_ctor cpp_fun_property cpp_fun_new_dynamic cpp_fun_jacobian cpp_fun_hessian cpp_fun_forward cpp_fun_reverse cpp_fun_optimize

cpp_fun_jacobian-> fun_jacobian_xam.cpp

Headings-> Syntax f f(x) x J Example

@(@\newcommand{\B}[1]{ {\bf #1} } \newcommand{\R}[1]{ {\rm #1} }@)@ Jacobian of an AD Function
Syntax
J = f.jacobian(x)

f
This is either a d_fun or a_fun function object. Upon return, the zero order Taylor coefficients in f correspond to the value of x . The other Taylor coefficients in f are unspecified.

f(x)
We use the notation @(@ f: \B{R}^n \rightarrow \B{R}^m @)@ for the function corresponding to f . Note that n is the size of ax and m is the size of ay in to the constructor for f .

x
If f is a d_fun or a_fun, this argument has prototype
     const vec_double&   x
     const vec_a_double& x
and its size must be n . It specifies the argument value at we are computing the Jacobian @(@ f'(x) @)@.

J
If f is a d_fun or a_fun, the result has prototype
     vec_double   J
     vec_a_double J
respectively and its size is m*n . For i between zero and m-1 and j between zero and n-1 , @[@ J [ i * n + j ] = \frac{ \partial f_i }{ \partial x_j } (x) @]@

Example
fun_jacobian_xam.cpp

---

Input File: lib/cplusplus/fun.cpp