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library-> py_lib cpp_lib

py_lib-> py_fun py_sparse py_utility more_py

py_fun-> py_independent py_abort_recording py_fun_ctor py_fun_property py_fun_new_dynamic py_fun_jacobian py_fun_hessian py_fun_forward py_fun_reverse py_fun_optimize

py_fun_optimize-> fun_optimize_xam.py

fun_optimize_xam.py

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def fun_optimize_xam() :
     #
     import numpy
     import cppad_py
     #
     # initialize return variable
     ok = True
     # ---------------------------------------------------------------------
     n_ind = 1 # number of independent variables
     n_dep = 1 # number of dependent variables
     n_var = 1 # phantom variable at address 0
     n_op  = 1 # special operator at beginning
     #
     # dimension some vectors
     x  = numpy.empty(n_ind, dtype=float)
     ay = numpy.empty(n_dep, dtype=cppad_py.a_double)
     #
     # independent variables
     x[0]  = 1.0
     ax    = cppad_py.independent(x)
     n_var = n_var + n_ind # one for each indpendent
     n_op  = n_op + n_ind
     #
     # accumulate summation
     ax0   = ax[0]
     csum  = cppad_py.a_double(0.0)
     csum  = ax0 + ax0 + ax0 + ax0
     n_var = n_var + 3 # one per + operator
     n_op  = n_op + 3
     #
     # define f(x) = y_0 = csum
     ay[0] = csum
     f     = cppad_py.d_fun(ax, ay)
     n_op  = n_op + 1 # speical operator at end
     #
     # check number of variables and operators
     ok = ok and f.size_var() == n_var
     ok = ok and f.size_op() == n_op
     #
     # optimize
     f.optimize()
     #
     # number of variables and operators has decreased by two
     ok = ok and f.size_var() == n_var-2
     ok = ok and f.size_op() == n_op-2
     #
     return( ok  )
#