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Up-> CppAD speed speed_adolc adolc_sparse_hessian.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_sparse_hessian.cpp

Headings-> Specifications Implementation

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

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

bool link_sparse_hessian(
     size_t                           size     ,
     size_t                           repeat   ,
     const CppAD::vector<size_t>&     row      ,
     const CppAD::vector<size_t>&     col      ,
     CppAD::vector<double>&           x_return ,
     CppAD::vector<double>&           hessian  ,
     size_t&                          n_sweep )
{
     if( global_option["atomic"] || (! global_option["colpack"]) )
          return false;
     if( global_option["memory"] || global_option["optimize"] || global_option["boolsparsity"] )
          return false;
     // -----------------------------------------------------
     // setup
     typedef unsigned int*    SizeVector;
     typedef double*          DblVector;
     typedef adouble          ADScalar;
     typedef ADScalar*        ADVector;


     size_t i, j, k;         // temporary indices
     size_t order = 0;    // derivative order corresponding to function
     size_t m = 1;        // number of dependent variables
     size_t n = size;     // number of independent variables

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

     // tape identifier
     int tag  = 0;
     // AD domain space vector
     ADVector a_x = thread_alloc::create_array<ADScalar>(n, capacity);
     // AD range space vector
     ADVector a_y = thread_alloc::create_array<ADScalar>(m, capacity);
     // double argument value
     DblVector x = thread_alloc::create_array<double>(n, capacity);
     // double function value
     double f;

     // options that control sparse_hess
     int        options[2];
     options[0] = 0; // safe mode
     options[1] = 0; // indirect recovery

     // structure that holds some of the work done by sparse_hess
     int        nnz;                   // number of non-zero values
     SizeVector rind   = CPPAD_NULL;   // row indices
     SizeVector cind   = CPPAD_NULL;   // column indices
     DblVector  values = CPPAD_NULL;   // Hessian values

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

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

          // AD computation of f (x)
          CppAD::sparse_hes_fun<ADScalar>(n, a_x, row, col, order, a_y);

          // create function object f : x -> y
          a_y[0] >>= f;
          trace_off();

          // is this a repeat call with the same sparsity pattern
          int same_pattern = 0;

          // calculate the hessian at this x
          rind   = CPPAD_NULL;
          cind   = CPPAD_NULL;
          values = CPPAD_NULL;
          sparse_hess(tag, int(n),
               same_pattern, x, &nnz, &rind, &cind, &values, options
          );
          // only needed last time through loop
          if( repeat == 0 )
          {     size_t K = row.size();
               for(int ell = 0; ell < nnz; ell++)
               {     i = size_t(rind[ell]);
                    j = size_t(cind[ell]);
                    for(k = 0; k < K; k++)
                    {     if( (row[k]==i && col[k]==j) || (row[k]==j && col[k]==i) )
                              hessian[k] = values[ell];
                    }
               }
          }

          // free raw memory allocated by sparse_hess
          free(rind);
          free(cind);
          free(values);
     }
     else
     {     // choose a value for x
          CppAD::uniform_01(n, x);

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

          // AD computation of f (x)
          CppAD::sparse_hes_fun<ADScalar>(n, a_x, row, col, order, a_y);

          // create function object f : x -> y
          a_y[0] >>= f;
          trace_off();

          // is this a repeat call at the same argument
          int same_pattern = 0;

          while(repeat--)
          {     // choose a value for x
               CppAD::uniform_01(n, x);

               // calculate the hessian at this x
               sparse_hess(tag, int(n),
                    same_pattern, x, &nnz, &rind, &cind, &values, options
               );
               same_pattern = 1;
          }
          size_t K = row.size();
          for(int ell = 0; ell < nnz; ell++)
          {     i = size_t(rind[ell]);
               j = size_t(cind[ell]);
               for(k = 0; k < K; k++)
               {     if( (row[k]==i && col[k]==j) || (row[k]==j && col[k]==i) )
                         hessian[k] = values[ell];
               }
          }
          // free raw memory allocated by sparse_hessian
          free(rind);
          free(cind);
          free(values);
     }
     // --------------------------------------------------------------------
     // return argument
     for(j = 0; j < n; j++)
          x_return[j] = x[j];

     // do not know how to return number of sweeps used
     n_sweep = 0;

     // tear down
     thread_alloc::delete_array(a_x);
     thread_alloc::delete_array(a_y);
     thread_alloc::delete_array(x);
     return true;

}