\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)

sacado_mat_mul.cpp

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Sacado Speed: Matrix Multiplication

Specifications

See link_mat_mul .

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;
}