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Up-> CppAD speed speed_fadbad fadbad_mat_mul.cpp

CppAD-> Install Introduction AD ADFun preprocessor multi_thread utility ipopt_solve Example speed Appendix

speed-> speed_main speed_utility speed_double speed_adolc speed_cppad speed_fadbad speed_sacado

speed_fadbad-> fadbad_det_minor.cpp fadbad_det_lu.cpp fadbad_mat_mul.cpp fadbad_ode.cpp fadbad_poly.cpp fadbad_sparse_hessian.cpp fadbad_sparse_jacobian.cpp

fadbad_mat_mul.cpp

Headings-> Specifications Implementation

// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <FADBAD++/badiff.h>
# include <cppad/speed/mat_sum_sq.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/utility/vector.hpp>

// list of possible options
# include <map>
extern std::map<std::string, bool> global_option;

bool link_mat_mul(
     size_t                           size     ,
     size_t                           repeat   ,
     CppAD::vector<double>&           x        ,
     CppAD::vector<double>&           z        ,
     CppAD::vector<double>&           dz       )
{
     // speed test global option values
     if( global_option["memory"] || global_option["onetape"] || global_option["atomic"] || global_option["optimize"] )
          return false;
     // The correctness check for this test is failing, so abort (for now).
     return false;

     // -----------------------------------------------------
     // setup

     // object for computing determinant
     typedef fadbad::B<double>       ADScalar;
     typedef CppAD::vector<ADScalar> ADVector;

     size_t j;                // temporary index
     size_t m = 1;            // number of dependent variables
     size_t n = size * size;  // number of independent variables
     ADVector   X(n);         // AD domain space vector
     ADVector   Y(n);         // Store product matrix
     ADVector   Z(m);         // AD range space vector

     // ------------------------------------------------------
     while(repeat--)
     {     // get the next matrix
          CppAD::uniform_01(n, x);

          // set independent variable values
          for(j = 0; j < n; j++)
               X[j] = x[j];

          // do the computation
          mat_sum_sq(size, X, Y, Z);

          // create function object f : X -> Z
          Z[0].diff(0, m);  // index 0 of m dependent variables

          // evaluate and return gradient using reverse mode
          for(j = 0; j < n; j++)
               dz[j] = X[j].d(0); // partial Z[0] w.r.t X[j]
     }
     // return function value
     z[0] = Z[0].x();

     // ---------------------------------------------------------
     return true;
}