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package sparse_jac_pattern_xam;
sub sparse_jac_pattern_xam() {
# check for standard perl programming conventions
use strict;
use warnings;
#
# load the Cppad Swig library
use pm_cppad;
#
# initilaize return variable
my $ok = 1;
# ---------------------------------------------------------------------
# number of dependent and independent variables
my $n = 3;
#
# create the independent variables ax
my $x = new pm_cppad::vec_double($n);
for(my $i = 0; $i < $n ; $i++) {
$x->set($i, $i + 2.0);
}
my $ax = pm_cppad::independent($x);
#
# create dependent variables ay with ay[i] = ax[j]
# where i = mod(j + 1, n)
my $ay = new pm_cppad::vec_a_double($n);
for(my $j = 0; $j < $n ; $j++) {
my $i = $j+1;
if( $i >= $n ) {
$i = $i - $n;
}
my $ay_i = $ax->get($j);
$ay->set($i, $ay_i);
}
#
# define af corresponding to f(x)
my $af = new pm_cppad::a_fun($ax, $ay);
#
# sparsity pattern for identity matrix
my $pat_in = new pm_cppad::sparse_rc();
$pat_in->resize($n, $n, $n);
for(my $k = 0; $k < $n; $k++) {
$pat_in->put($k, $k, $k);
}
#
# loop over forward and reverse mode
for(my $mode = 0; $mode < 2; $mode++) {
my $pat_out = new pm_cppad::sparse_rc();
if( $mode == 0 ) {
$af->for_jac_sparsity($pat_in, $pat_out);
}
if( $mode == 1 ) {
$af->rev_jac_sparsity($pat_in, $pat_out);
}
#
# check that result is sparsity pattern for Jacobian
$ok = $ok && $n == $pat_out->nnz();
my $col_major = $pat_out->col_major();
my $row = $pat_out->row();
my $col = $pat_out->col();
for(my $k = 0; $k < $n; $k++) {
my $ell = $col_major->get($k);
my $r = $row->get($ell);
my $c = $col->get($ell);
my $i = $c+1;
if( $i >= $n ) {
$i = $i - $n;
}
$ok = $ok && $c == $k;
$ok = $ok && $r == $i;
}
}
#
return( $ok );
}