Fadbad Speed: Matrix Multiplication
Keywords: fadbad speed: matrix multiplication link_mat_mul speed multiply specifications implementation

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Fadbad Speed: Matrix Multiplication
Specifications
See link_mat_mul .
Implementation `

    # 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
    extern bool global_memory, global_onetape, global_atomic, global_optimize;

    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_memory || global_onetape || global_atomic || global_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;
    }

`

Input File: speed/fadbad/mat_mul.cpp