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Up-> CppAD speed speed_sacado sacado_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_sacado-> sacado_det_minor.cpp sacado_det_lu.cpp sacado_mat_mul.cpp sacado_ode.cpp sacado_poly.cpp sacado_sparse_hessian.cpp sacado_sparse_jacobian.cpp

sacado_mat_mul.cpp

Headings-> Specifications Implementation

// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <Sacado.hpp>
# include <cppad/utility/vector.hpp>
# include <cppad/speed/mat_sum_sq.hpp>
# include <cppad/speed/uniform_01.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;
     // -----------------------------------------------------
     // setup

     // object for computing determinant
     typedef Sacado::Rad::ADvar<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
     ADScalar   f;

     // ------------------------------------------------------
     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
          f = Z[0];

          // reverse mode gradient of last ADvar computed value; i.e., f
          ADScalar::Gradcomp();

          // return gradient
          for(j = 0; j < n; j++)
               dz[j] = X[j].adj(); // partial f w.r.t X[j]
     }
     // return function value
     z[0] = f.val();

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