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Up-> cppad_swig library a_fun a_fun_forward a_fun_forward_xam.m

cppad_swig-> testing swig_xam.i library whats_new_2018

library-> module a_double vector a_fun sparse error xam.m4

a_fun-> independent abort_recording a_fun_ctor a_fun_jacobian a_fun_hessian a_fun_forward a_fun_reverse a_fun_optimize a_fun_property

a_fun_forward-> a_fun_forward_xam.cpp a_fun_forward_xam.m a_fun_forward_xam.pm a_fun_forward_xam.py

a_fun_forward_xam.m

Headings

function ok = a_fun_forward_xam()
     %
     % load the Cppad Swig library
     m_cppad
     %
     % initialize return variable
     ok = true;
     % -----------------------------------------------------------------------
     % number of dependent and independent variables
     n_dep = 1;
     n_ind = 2;
     %
     % create the independent variables ax
     xp = m_cppad.vec_double(n_ind);
     for i = [ 0 :(n_ind -1) ]
          xp(i) = i + 1.0;
     end
     ax = m_cppad.independent(xp);
     %
     % create dependent varialbes ay with ay0 = ax0 * ax1
     ax0 = ax(0);
     ax1 = ax(1);
     ay = m_cppad.vec_a_double(n_dep);
     ay(0) = ax0 * ax1;
     %
     % define af corresponding to f(x) = x0 * x1
     af = m_cppad.a_fun(ax, ay);
     %
     % define X(t) = (3 + t, 2 + t)
     % it follows that Y(t) = f(X(t)) = (3 + t) * (2 + t)
     %
     % Y(0) = 6 and p ! = 1
     p = 0;
     xp(0) = 3.0;
     xp(1) = 2.0;
     yp = af.forward(p, xp);
     ok = ok && yp(0) == 6.0;
     %
     % first order Taylor coefficients for X(t)
     p = 1;
     xp(0) = 1.0;
     xp(1) = 1.0;
     %
     % first order Taylor coefficient for Y(t)
     % Y'(0) = 3 + 2 = 5 and p ! = 1
     yp = af.forward(p, xp);
     ok = ok && yp(0) == 5.0;
     %
     % second order Taylor coefficients for X(t)
     p = 2;
     xp(0) = 0.0;
     xp(1) = 0.0;
     %
     % second order Taylor coefficient for Y(t)
     % Y''(0) = 2.0 and p ! = 2
     yp = af.forward(p, xp);
     ok = ok && yp(0) == 1.0;
     %
     return;
end