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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 rev_hes_sparsity.cpp}
Reverse Mode Hessian Sparsity: Example and Test
###############################################
{xrst_literal
// BEGIN C++
// END C++
}
{xrst_end rev_hes_sparsity.cpp}
*/
// BEGIN C++
# include <cppad/cppad.hpp>
bool rev_hes_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 = 3;
CPPAD_TESTVECTOR(AD<double>) ax(n);
ax[0] = 0.;
ax[1] = 1.;
ax[2] = 2.;
// declare independent variables and start recording
CppAD::Independent(ax);
// range space vector
size_t m = 2;
CPPAD_TESTVECTOR(AD<double>) ay(m);
ay[0] = sin( ax[2] );
ay[1] = ax[0] * ax[1];
// create f: x -> y and stop tape recording
CppAD::ADFun<double> f(ax, ay);
// sparsity pattern for the identity matrix
size_t nr = n;
size_t nc = n;
size_t nnz_in = n;
sparsity pattern_in(nr, nc, nnz_in);
for(size_t k = 0; k < nnz_in; k++)
{ size_t r = k;
size_t c = k;
pattern_in.set(k, r, c);
}
// compute sparsity pattern for J(x) = F'(x)
bool transpose = false;
bool dependency = false;
bool internal_bool = false;
sparsity pattern_out;
f.for_jac_sparsity(
pattern_in, transpose, dependency, internal_bool, pattern_out
);
//
// compute sparsity pattern for H(x) = F_1''(x)
CPPAD_TESTVECTOR(bool) select_range(m);
select_range[0] = false;
select_range[1] = true;
f.rev_hes_sparsity(
select_range, transpose, internal_bool, pattern_out
);
size_t nnz = pattern_out.nnz();
ok &= nnz == 2;
ok &= pattern_out.nr() == n;
ok &= pattern_out.nc() == n;
{ // check results
const SizeVector& row( pattern_out.row() );
const SizeVector& col( pattern_out.col() );
SizeVector row_major = pattern_out.row_major();
//
ok &= row[ row_major[0] ] == 0 && col[ row_major[0] ] == 1;
ok &= row[ row_major[1] ] == 1 && col[ row_major[1] ] == 0;
}
//
// compute sparsity pattern for H(x) = F_0''(x)
select_range[0] = true;
select_range[1] = false;
f.rev_hes_sparsity(
select_range, transpose, internal_bool, pattern_out
);
nnz = pattern_out.nnz();
ok &= nnz == 1;
ok &= pattern_out.nr() == n;
ok &= pattern_out.nc() == n;
{ // check results
const SizeVector& row( pattern_out.row() );
const SizeVector& col( pattern_out.col() );
//
ok &= row[0] == 2 && col[0] == 2;
}
return ok;
}
// END C++
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