# include <cppad/cppad.hpp>
namespace { // ---------------------------------------------------------
bool reverse()
{ bool ok = true;
using CppAD::AD;
using CppAD::NearEqual;
typedef CPPAD_TESTVECTOR(AD<double>) a_vector;
typedef CPPAD_TESTVECTOR(double) d_vector;
typedef CPPAD_TESTVECTOR(size_t) i_vector;
size_t i, j, k, ell;
double eps = 10. * CppAD::numeric_limits<double>::epsilon();
// domain space vector
size_t n = 4;
a_vector a_x(n);
for(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(j = 0; j < n; j++)
x[j] = double(j);
// Jacobian of y without sparsity pattern
d_vector jac(m * n);
jac = f.SparseJacobian(x);
/*
[ 1 1 0 0 ]
jac = [ 0 0 1 1 ]
[ 1 1 1 x_3]
*/
d_vector check(m * n);
check[0] = 1.; check[1] = 1.; check[2] = 0.; check[3] = 0.;
check[4] = 0.; check[5] = 0.; check[6] = 1.; check[7] = 1.;
check[8] = 1.; check[9] = 1.; check[10] = 1.; check[11] = x[3];
for(ell = 0; ell < size_t(check.size()); ell++)
ok &= NearEqual(check[ell], jac[ell], eps, eps );
// using packed boolean sparsity patterns
CppAD::vectorBool s_b(m * m), p_b(m * n);
for(i = 0; i < m; i++)
{ for(ell = 0; ell < m; ell++)
s_b[i * m + ell] = false;
s_b[i * m + i] = true;
}
p_b = f.RevSparseJac(m, s_b);
jac = f.SparseJacobian(x, p_b);
for(ell = 0; ell < size_t(check.size()); ell++)
ok &= NearEqual(check[ell], jac[ell], eps, eps );
// using vector of sets sparsity patterns
std::vector< std::set<size_t> > s_s(m), p_s(m);
for(i = 0; i < m; i++)
s_s[i].insert(i);
p_s = f.RevSparseJac(m, s_s);
jac = f.SparseJacobian(x, p_s);
for(ell = 0; ell < size_t(check.size()); ell++)
ok &= NearEqual(check[ell], jac[ell], eps, eps );
// using row and column indices to compute non-zero in rows 1 and 2
// (skip row 0).
size_t K = 6;
i_vector row(K), col(K);
jac.resize(K);
k = 0;
for(j = 0; j < n; j++)
{ for(i = 1; i < m; i++)
{ ell = i * n + j;
if( p_b[ell] )
{ ok &= check[ell] != 0.;
row[k] = i;
col[k] = j;
k++;
}
}
}
ok &= k == K;
// empty work structure
CppAD::sparse_jacobian_work work;
// could use p_b
size_t n_sweep = f.SparseJacobianReverse(x, p_s, row, col, jac, work);
for(k = 0; k < K; k++)
{ ell = row[k] * n + col[k];
ok &= NearEqual(check[ell], jac[k], eps, eps);
}
ok &= n_sweep == 2;
// now recompute at a different x value (using work from previous call)
check[11] = x[3] = 10.;
std::vector< std::set<size_t> > not_used;
n_sweep = f.SparseJacobianReverse(x, not_used, row, col, jac, work);
for(k = 0; k < K; k++)
{ ell = row[k] * n + col[k];
ok &= NearEqual(check[ell], jac[k], eps, eps);
}
ok &= n_sweep == 2;
return ok;
}
bool forward()
{ bool ok = true;
using CppAD::AD;
using CppAD::NearEqual;
typedef CPPAD_TESTVECTOR(AD<double>) a_vector;
typedef CPPAD_TESTVECTOR(double) d_vector;
typedef CPPAD_TESTVECTOR(size_t) i_vector;
size_t i, j, k, ell;
double eps = 10. * CppAD::numeric_limits<double>::epsilon();
// domain space vector
size_t n = 3;
a_vector a_x(n);
for(j = 0; j < n; j++)
a_x[j] = AD<double> (0);
// declare independent variables and starting recording
CppAD::Independent(a_x);
size_t m = 4;
a_vector a_y(m);
a_y[0] = a_x[0] + a_x[2];
a_y[1] = a_x[0] + a_x[2];
a_y[2] = a_x[1] + a_x[2];
a_y[3] = a_x[1] + a_x[2] * a_x[2] / 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(j = 0; j < n; j++)
x[j] = double(j);
// Jacobian of y without sparsity pattern
d_vector jac(m * n);
jac = f.SparseJacobian(x);
/*
[ 1 0 1 ]
jac = [ 1 0 1 ]
[ 0 1 1 ]
[ 0 1 x_2 ]
*/
d_vector check(m * n);
check[0] = 1.; check[1] = 0.; check[2] = 1.;
check[3] = 1.; check[4] = 0.; check[5] = 1.;
check[6] = 0.; check[7] = 1.; check[8] = 1.;
check[9] = 0.; check[10] = 1.; check[11] = x[2];
for(ell = 0; ell < size_t(check.size()); ell++)
ok &= NearEqual(check[ell], jac[ell], eps, eps );
// test using packed boolean vectors for sparsity pattern
CppAD::vectorBool r_b(n * n), p_b(m * n);
for(j = 0; j < n; j++)
{ for(ell = 0; ell < n; ell++)
r_b[j * n + ell] = false;
r_b[j * n + j] = true;
}
p_b = f.ForSparseJac(n, r_b);
jac = f.SparseJacobian(x, p_b);
for(ell = 0; ell < size_t(check.size()); ell++)
ok &= NearEqual(check[ell], jac[ell], eps, eps );
// test using vector of sets for sparsity pattern
std::vector< std::set<size_t> > r_s(n), p_s(m);
for(j = 0; j < n; j++)
r_s[j].insert(j);
p_s = f.ForSparseJac(n, r_s);
jac = f.SparseJacobian(x, p_s);
for(ell = 0; ell < size_t(check.size()); ell++)
ok &= NearEqual(check[ell], jac[ell], eps, eps );
// using row and column indices to compute non-zero elements excluding
// row 0 and column 0.
size_t K = 5;
i_vector row(K), col(K);
jac.resize(K);
k = 0;
for(i = 1; i < m; i++)
{ for(j = 1; j < n; j++)
{ ell = i * n + j;
if( p_b[ell] )
{ ok &= check[ell] != 0.;
row[k] = i;
col[k] = j;
k++;
}
}
}
ok &= k == K;
// empty work structure
CppAD::sparse_jacobian_work work;
// could use p_s
size_t n_sweep = f.SparseJacobianForward(x, p_b, row, col, jac, work);
for(k = 0; k < K; k++)
{ ell = row[k] * n + col[k];
ok &= NearEqual(check[ell], jac[k], eps, eps);
}
ok &= n_sweep == 2;
// now recompute at a different x value (using work from previous call)
check[11] = x[2] = 10.;
n_sweep = f.SparseJacobianForward(x, p_s, row, col, jac, work);
for(k = 0; k < K; k++)
{ ell = row[k] * n + col[k];
ok &= NearEqual(check[ell], jac[k], eps, eps);
}
ok &= n_sweep == 2;
return ok;
}
} // End empty namespace
bool sparse_jacobian(void)
{ bool ok = true;
ok &= forward();
ok &= reverse();
return ok;
}
