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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 subgraph_sparsity.cpp}
Subgraph Dependency Sparsity Patterns: Example and Test
#######################################################
{xrst_literal
// BEGIN C++
// END C++
}
{xrst_end subgraph_sparsity.cpp}
*/
// BEGIN C++
# include <cppad/cppad.hpp>
bool subgraph_sparsity(void)
{ bool ok = true;
using CppAD::AD;
typedef CPPAD_TESTVECTOR(size_t) SizeVector;
typedef CppAD::sparse_rc<SizeVector> sparsity;
//
// domain space vector
size_t n = 2;
CPPAD_TESTVECTOR(AD<double>) ax(n);
ax[0] = 0.;
ax[1] = 1.;
// declare independent variables and start recording
CppAD::Independent(ax);
// range space vector
size_t m = 3;
CPPAD_TESTVECTOR(AD<double>) ay(m);
ay[0] = ax[0];
ay[1] = ax[0] * ax[1];
ay[2] = ax[1];
// create f: x -> y and stop tape recording
CppAD::ADFun<double> f(ax, ay);
ok &= f.size_random() == 0;
// compute sparsite pattern for F'(x)
CPPAD_TESTVECTOR(bool) select_domain(n), select_range(m);
for(size_t j = 0; j < n; j++)
select_domain[j] = true;
for(size_t i = 0; i < m; i++)
select_range[i] = true;
bool transpose = false;
sparsity pattern_out;
f.subgraph_sparsity(select_domain, select_range, transpose, pattern_out);
// check sparsity pattern
size_t nnz = pattern_out.nnz();
ok &= nnz == 4;
ok &= pattern_out.nr() == m;
ok &= pattern_out.nc() == n;
{ // check results
const SizeVector& row( pattern_out.row() );
const SizeVector& col( pattern_out.col() );
SizeVector col_major = pattern_out.col_major();
//
ok &= row[ col_major[0] ] == 0 && col[ col_major[0] ] == 0;
ok &= row[ col_major[1] ] == 1 && col[ col_major[1] ] == 0;
ok &= row[ col_major[2] ] == 1 && col[ col_major[2] ] == 1;
ok &= row[ col_major[3] ] == 2 && col[ col_major[3] ] == 1;
}
// note that the transpose of the identity is the identity
transpose = true;
f.subgraph_sparsity(select_domain, select_range, transpose, pattern_out);
//
nnz = pattern_out.nnz();
ok &= nnz == 4;
ok &= pattern_out.nr() == n;
ok &= pattern_out.nc() == m;
{ // check results
const SizeVector& row( pattern_out.row() );
const SizeVector& col( pattern_out.col() );
SizeVector row_major = pattern_out.row_major();
//
ok &= col[ row_major[0] ] == 0 && row[ row_major[0] ] == 0;
ok &= col[ row_major[1] ] == 1 && row[ row_major[1] ] == 0;
ok &= col[ row_major[2] ] == 1 && row[ row_major[2] ] == 1;
ok &= col[ row_major[3] ] == 2 && row[ row_major[3] ] == 1;
}
ok &= f.size_random() > 0;
f.clear_subgraph();
ok &= f.size_random() == 0;
return ok;
}
// END C++
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