/*!
Link from user_atomic to forward sparse Jacobian
\copydetails atomic_base::rev_sparse_hes
*/
virtual bool rev_sparse_hes(
const vector<bool>& vx ,
const vector<bool>& s ,
vector<bool>& t ,
size_t q ,
const vector< std::set<size_t> >& r ,
const vector< std::set<size_t> >& u ,
vector< std::set<size_t> >& v )
{ size_t n = v.size();
size_t m = u.size();
CPPAD_ASSERT_UNKNOWN( r.size() == v.size() );
CPPAD_ASSERT_UNKNOWN( s.size() == m );
CPPAD_ASSERT_UNKNOWN( t.size() == n );
bool ok = true;
bool transpose = true;
std::set<size_t>::const_iterator itr;
// compute sparsity pattern for T(x) = S(x) * f'(x)
t = f_.RevSparseJac(1, s);
# ifndef NDEBUG
for(size_t j = 0; j < n; j++)
CPPAD_ASSERT_UNKNOWN( vx[j] || ! t[j] )
# endif
// V(x) = f'(x)^T * g''(y) * f'(x) * R + g'(y) * f''(x) * R
// U(x) = g''(y) * f'(x) * R
// S(x) = g'(y)
// compute sparsity pattern for A(x) = f'(x)^T * U(x)
vector< std::set<size_t> > a(n);
a = f_.RevSparseJac(q, u, transpose);
// set version of s
vector< std::set<size_t> > set_s(1);
CPPAD_ASSERT_UNKNOWN( set_s[0].empty() );
size_t i;
for(i = 0; i < m; i++)
if( s[i] )
set_s[0].insert(i);
// compute sparsity pattern for H(x) = (S(x) * F)''(x) * R
// (store it in v)
f_.ForSparseJac(q, r);
v = f_.RevSparseHes(q, set_s, transpose);
// compute sparsity pattern for V(x) = A(x) + H(x)
for(i = 0; i < n; i++)
{ for(itr = a[i].begin(); itr != a[i].end(); itr++)
{ size_t j = *itr;
CPPAD_ASSERT_UNKNOWN( j < q );
v[i].insert(j);
}
}
// no longer need the forward mode sparsity pattern
// (have to reconstruct them every time)
f_.size_forward_set(0);
return ok;
}
/*!
Link from user_atomic to forward sparse Jacobian
\copydetails atomic_base::rev_sparse_hes
*/
virtual bool rev_sparse_hes(
const vector<bool>& vx ,
const vector<bool>& s ,
vector<bool>& t ,
size_t q ,
const vector<bool>& r ,
const vector<bool>& u ,
vector<bool>& v )
{
CPPAD_ASSERT_UNKNOWN( r.size() == v.size() );
CPPAD_ASSERT_UNKNOWN( s.size() == u.size() / q );
CPPAD_ASSERT_UNKNOWN( t.size() == v.size() / q );
size_t n = t.size();
bool ok = true;
bool transpose = true;
std::set<size_t>::const_iterator itr;
size_t i, j;
// compute sparsity pattern for T(x) = S(x) * f'(x)
t = f_.RevSparseJac(1, s);
# ifndef NDEBUG
for(j = 0; j < n; j++)
CPPAD_ASSERT_UNKNOWN( vx[j] || ! t[j] )
# endif
// V(x) = f'(x)^T * g''(y) * f'(x) * R + g'(y) * f''(x) * R
// U(x) = g''(y) * f'(x) * R
// S(x) = g'(y)
// compute sparsity pattern for A(x) = f'(x)^T * U(x)
vector<bool> a(n * q);
a = f_.RevSparseJac(q, u, transpose);
// compute sparsity pattern for H(x) =(S(x) * F)''(x) * R
// (store it in v)
f_.ForSparseJac(q, r);
v = f_.RevSparseHes(q, s, transpose);
// compute sparsity pattern for V(x) = A(x) + H(x)
for(i = 0; i < n; i++)
{ for(j = 0; j < q; j++)
v[ i * q + j ] |= a[ i * q + j];
}
// no longer need the forward mode sparsity pattern
// (have to reconstruct them every time)
f_.size_forward_set(0);
return ok;
}
/*!
Link from user_atomic to forward sparse Jacobian
\copydetails atomic_base::for_sparse_jac
*/
virtual bool for_sparse_jac(
size_t q ,
const vector< std::set<size_t> >& r ,
vector< std::set<size_t> >& s )
{
bool ok = true;
s = f_.ForSparseJac(q, r);
// no longer need the forward mode sparsity pattern
// (have to reconstruct them every time)
f_.size_forward_set(0);
return ok;
}
