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// SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
// SPDX-FileCopyrightText: Bradley M. Bell <bradbell@seanet.com>
// SPDX-FileContributor: 2003-22 Bradley M. Bell
// ----------------------------------------------------------------------------
/*
{xrst_begin colpack_jac.cpp}
ColPack: Sparse Jacobian Example and Test
#########################################
{xrst_literal
// BEGIN C++
// END C++
}
{xrst_end colpack_jac.cpp}
*/
// BEGIN C++
# include <cppad/cppad.hpp>
bool colpack_jac(void)
{ bool ok = true;
using CppAD::AD;
using CppAD::NearEqual;
typedef CPPAD_TESTVECTOR(AD<double>) a_vector;
typedef CPPAD_TESTVECTOR(double) d_vector;
typedef CppAD::vector<size_t> i_vector;
typedef CppAD::sparse_rc<i_vector> sparsity;
typedef CppAD::sparse_rcv<i_vector, d_vector> sparse_matrix;
// domain space vector
size_t n = 4;
a_vector a_x(n);
for(size_t j = 0; j < n; j++)
a_x[j] = AD<double> (0);
// declare independent variables and starting recording
CppAD::Independent(a_x);
size_t m = 3;
a_vector a_y(m);
a_y[0] = a_x[0] + a_x[1];
a_y[1] = a_x[2] + a_x[3];
a_y[2] = a_x[0] + a_x[1] + a_x[2] + a_x[3] * a_x[3] / 2.;
// create f: x -> y and stop tape recording
CppAD::ADFun<double> f(a_x, a_y);
// new value for the independent variable vector
d_vector x(n);
for(size_t j = 0; j < n; j++)
x[j] = double(j);
/*
[ 1 1 0 0 ]
jac = [ 0 0 1 1 ]
[ 1 1 1 x_3]
*/
// Normally one would use CppAD to compute sparsity pattern, but for this
// example we set it directly
size_t nr = m;
size_t nc = n;
size_t nnz = 8;
sparsity pattern(nr, nc, nnz);
d_vector check(nnz);
for(size_t k = 0; k < nnz; k++)
{ size_t r, c;
if( k < 2 )
{ r = 0;
c = k;
}
else if( k < 4 )
{ r = 1;
c = k;
}
else
{ r = 2;
c = k - 4;
}
pattern.set(k, r, c);
if( k == nnz - 1 )
check[k] = x[3];
else
check[k] = 1.0;
}
// using row and column indices to compute non-zero in rows 1 and 2
sparse_matrix subset( pattern );
// check results for both CppAD and Colpack
for(size_t i_method = 0; i_method < 4; i_method++)
{ // coloring method
std::string coloring;
if( i_method % 2 == 0 )
coloring = "cppad";
else
coloring = "colpack";
//
CppAD::sparse_jac_work work;
size_t group_max = 1;
if( i_method / 2 == 0 )
{ size_t n_sweep = f.sparse_jac_for(
group_max, x, subset, pattern, coloring, work
);
ok &= n_sweep == 4;
}
else
{ size_t n_sweep = f.sparse_jac_rev(
x, subset, pattern, coloring, work
);
ok &= n_sweep == 2;
}
const d_vector& hes( subset.val() );
for(size_t k = 0; k < nnz; k++)
ok &= check[k] == hes[k];
}
return ok;
}
// END C++
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