Prev

Next

Index-> contents reference index search external

Up-> CppAD speed speed_sacado sacado_det_lu.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_det_lu.cpp

Headings-> Specifications Implementation

// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//

# include <Sacado.hpp>
# include <cppad/speed/det_by_lu.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_det_lu(
     size_t                     size     ,
     size_t                     repeat   ,
     CppAD::vector<double>     &matrix   ,
     CppAD::vector<double>     &gradient )
{
     // speed test global option values
     if( global_option["onetape"] || global_option["atomic"] )
          return false;
     if( global_option["memory"] || global_option["optimize"] )
          return false;
     // -----------------------------------------------------
     // setup
     //
     // object for computing determinant
     typedef Sacado::Rad::ADvar<double>   ADScalar;
     typedef CppAD::vector<ADScalar>      ADVector;
     CppAD::det_by_lu<ADScalar>           Det(size);

     size_t i;                // temporary index
     size_t n = size * size;  // number of independent variables
     ADScalar   detA;         // AD value of the determinant
     ADVector   A(n);         // AD version of matrix

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

          // set independent variable values
          for(i = 0; i < n; i++)
               A[i] = matrix[i];

          // compute the determinant
          detA = Det(A);

          // compute the gradient of detA
          ADScalar::Gradcomp();

          // evaluate and return gradient using reverse mode
          for(i =0; i < n; i++)
               gradient[i] = A[i].adj(); // partial detA w.r.t A[i]
     }
     // ---------------------------------------------------------
     return true;
}