Prev

Next

Index-> contents reference index search external

Up-> CppAD speed speed_adolc adolc_det_minor.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_adolc-> adolc_det_minor.cpp adolc_det_lu.cpp adolc_mat_mul.cpp adolc_ode.cpp adolc_poly.cpp adolc_sparse_hessian.cpp adolc_sparse_jacobian.cpp adolc_alloc_mat

adolc_det_minor.cpp

Headings-> Specifications Implementation

// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <adolc/adolc.h>
# include <cppad/utility/vector.hpp>
# include <cppad/speed/det_by_minor.hpp>
# include <cppad/speed/uniform_01.hpp>

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

bool link_det_minor(
     size_t                     size     ,
     size_t                     repeat   ,
     CppAD::vector<double>     &matrix   ,
     CppAD::vector<double>     &gradient )
{
     // speed test global option values
     if( global_option["atomic"] )
          return false;
     if( global_option["memory"] || global_option["optimize"] )
          return false;
     // -----------------------------------------------------
     // setup
     typedef adouble    ADScalar;
     typedef ADScalar*  ADVector;

     int tag  = 0;         // tape identifier
     int m    = 1;         // number of dependent variables
     int n    = size*size; // number of independent variables
     double f;             // function value
     int j;                // temporary index

     // set up for thread_alloc memory allocator (fast and checks for leaks)
     using CppAD::thread_alloc; // the allocator
     size_t capacity;           // capacity of an allocation

     // object for computing determinant
     CppAD::det_by_minor<ADScalar> Det(size);

     // AD value of determinant
     ADScalar   detA;

     // AD version of matrix
     ADVector A   = thread_alloc::create_array<ADScalar>(size_t(n), capacity);

     // vectors of reverse mode weights
     double* u    = thread_alloc::create_array<double>(size_t(m), capacity);
     u[0] = 1.;

     // vector with matrix value
     double* mat  = thread_alloc::create_array<double>(size_t(n), capacity);

     // vector to receive gradient result
     double* grad = thread_alloc::create_array<double>(size_t(n), capacity);

     // ----------------------------------------------------------------------
     if( ! global_option["onetape"] ) while(repeat--)
     {     // choose a matrix
          CppAD::uniform_01(n, mat);

          // declare independent variables
          int keep = 1; // keep forward mode results
          trace_on(tag, keep);
          for(j = 0; j < n; j++)
               A[j] <<= mat[j];

          // AD computation of the determinant
          detA = Det(A);

          // create function object f : A -> detA
          detA >>= f;
          trace_off();

          // evaluate and return gradient using reverse mode
          fos_reverse(tag, m, n, u, grad);
     }
     else
     {
          // choose a matrix
          CppAD::uniform_01(n, mat);

          // declare independent variables
          int keep = 0; // do not keep forward mode results in buffer
          trace_on(tag, keep);
          for(j = 0; j < n; j++)
               A[j] <<= mat[j];

          // AD computation of the determinant
          detA = Det(A);

          // create function object f : A -> detA
          detA >>= f;
          trace_off();

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

               // evaluate the determinant at the new matrix value
               keep = 1; // keep this forward mode result
               zos_forward(tag, m, n, keep, mat, &f);

               // evaluate and return gradient using reverse mode
               fos_reverse(tag, m, n, u, grad);
          }
     }
     // --------------------------------------------------------------------

     // return matrix and gradient
     for(j = 0; j < n; j++)
     {     matrix[j] = mat[j];
          gradient[j] = grad[j];
     }

     // tear down
     thread_alloc::delete_array(grad);
     thread_alloc::delete_array(mat);
     thread_alloc::delete_array(u);
     thread_alloc::delete_array(A);
     return true;
}