Prev Next adolc_sparse_hessian.cpp

@(@\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}} }@)@
Adolc Speed: Sparse Hessian

Specifications
See link_sparse_hessian .

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;

}

Input File: speed/adolc/sparse_hessian.cpp