void operator()(
const ADVector& ax ,
ADVector& ay ,
size_t id = 0 )
{ size_t i, j;
size_t n = ax.size();
size_t m = ay.size();
# ifndef NDEBUG
bool ok;
std::string msg = "atomic_base: " + afun_name() + ".eval: ";
if( (n == 0) | (m == 0) )
{ msg += "ax.size() or ay.size() is zero";
CPPAD_ASSERT_KNOWN(false, msg.c_str() );
}
# endif
size_t thread = thread_alloc::thread_num();
vector <Base>& tx = afun_tx_[thread];
vector <Base>& ty = afun_ty_[thread];
vector <bool>& vx = afun_vx_[thread];
vector <bool>& vy = afun_vy_[thread];
//
if( vx.size() != n )
{ vx.resize(n);
tx.resize(n);
}
if( vy.size() != m )
{ vy.resize(m);
ty.resize(m);
}
//
// Determine tape corresponding to variables in ax
tape_id_t tape_id = 0;
ADTape<Base>* tape = CPPAD_NULL;
for(j = 0; j < n; j++)
{ tx[j] = ax[j].value_;
vx[j] = Variable( ax[j] );
if( vx[j] )
{
if( tape_id == 0 )
{ tape = ax[j].tape_this();
tape_id = ax[j].tape_id_;
CPPAD_ASSERT_UNKNOWN( tape != CPPAD_NULL );
}
# ifndef NDEBUG
if( tape_id != ax[j].tape_id_ )
{ msg += afun_name() +
": ax contains variables from different threads.";
CPPAD_ASSERT_KNOWN(false, msg.c_str());
}
# endif
}
}
// Use zero order forward mode to compute values
size_t q = 0, p = 0;
set_id(id);
# ifdef NDEBUG
forward(q, p, vx, vy, tx, ty);
# else
ok = forward(q, p, vx, vy, tx, ty);
if( ! ok )
{ msg += afun_name() + ": ok is false for "
"zero order forward mode calculation.";
CPPAD_ASSERT_KNOWN(false, msg.c_str());
}
# endif
bool record_operation = false;
for(i = 0; i < m; i++)
{
// pass back values
ay[i].value_ = ty[i];
// initialize entire vector parameters (not in tape)
ay[i].tape_id_ = 0;
ay[i].taddr_ = 0;
// we need to record this operation if
// any of the eleemnts of ay are variables,
record_operation |= vy[i];
}
# ifndef NDEBUG
if( record_operation & (tape == CPPAD_NULL) )
{ msg +=
"all elements of vx are false but vy contains a true element";
CPPAD_ASSERT_KNOWN(false, msg.c_str() );
}
# endif
// if tape is not null, ay is on the tape
if( record_operation )
{
// Operator that marks beginning of this atomic operation
CPPAD_ASSERT_UNKNOWN( NumRes(UserOp) == 0 );
CPPAD_ASSERT_UNKNOWN( NumArg(UserOp) == 4 );
tape->Rec_.PutArg(index_, id, n, m);
tape->Rec_.PutOp(UserOp);
// Now put n operators, one for each element of arugment vector
CPPAD_ASSERT_UNKNOWN( NumRes(UsravOp) == 0 );
CPPAD_ASSERT_UNKNOWN( NumRes(UsrapOp) == 0 );
CPPAD_ASSERT_UNKNOWN( NumArg(UsravOp) == 1 );
CPPAD_ASSERT_UNKNOWN( NumArg(UsrapOp) == 1 );
for(j = 0; j < n; j++)
{ if( vx[j] )
{ // information for an argument that is a variable
tape->Rec_.PutArg(ax[j].taddr_);
tape->Rec_.PutOp(UsravOp);
}
else
{ // information for an arugment that is parameter
addr_t par = tape->Rec_.PutPar(ax[j].value_);
tape->Rec_.PutArg(par);
tape->Rec_.PutOp(UsrapOp);
}
}
// Now put m operators, one for each element of result vector
CPPAD_ASSERT_UNKNOWN( NumArg(UsrrpOp) == 1 );
CPPAD_ASSERT_UNKNOWN( NumRes(UsrrpOp) == 0 );
CPPAD_ASSERT_UNKNOWN( NumArg(UsrrvOp) == 0 );
CPPAD_ASSERT_UNKNOWN( NumRes(UsrrvOp) == 1 );
for(i = 0; i < m; i++)
{ if( vy[i] )
{ ay[i].taddr_ = tape->Rec_.PutOp(UsrrvOp);
ay[i].tape_id_ = tape_id;
}
else
{ addr_t par = tape->Rec_.PutPar(ay[i].value_);
tape->Rec_.PutArg(par);
tape->Rec_.PutOp(UsrrpOp);
}
}
// Put a duplicate UserOp at end of UserOp sequence
tape->Rec_.PutArg(index_, id, n, m);
tape->Rec_.PutOp(UserOp);
}
return;
}
bool atomic_base<Base>::rev_sparse_hes(
const vector<Base>& x ,
const local::pod_vector<size_t>& x_index ,
const local::pod_vector<size_t>& y_index ,
const InternalSparsity& for_jac_sparsity ,
bool* rev_jac_flag ,
InternalSparsity& rev_hes_sparsity )
{ CPPAD_ASSERT_UNKNOWN( for_jac_sparsity.end() == rev_hes_sparsity.end() );
size_t q = rev_hes_sparsity.end();
size_t n = x_index.size();
size_t m = y_index.size();
bool ok = false;
size_t thread = thread_alloc::thread_num();
allocate_work(thread);
bool zero_empty = true;
bool input_empty = false;
bool transpose = false;
//
// vx
vector<bool> vx(n);
for(size_t j = 0; j < n; j++)
vx[j] = x_index[j] != 0;
//
// note that s and t are vectors so transpose does not matter for bool case
vector<bool> bool_s( work_[thread]->bool_s );
vector<bool> bool_t( work_[thread]->bool_t );
//
bool_s.resize(m);
bool_t.resize(n);
//
for(size_t i = 0; i < m; i++)
{ if( y_index[i] > 0 )
bool_s[i] = rev_jac_flag[ y_index[i] ];
}
//
std::string msg = ": atomic_base.rev_sparse_hes: returned false";
if( sparsity_ == pack_sparsity_enum )
{ vectorBool& pack_r( work_[thread]->pack_r );
vectorBool& pack_u( work_[thread]->pack_u );
vectorBool& pack_v( work_[thread]->pack_h );
//
pack_v.resize(n * q);
//
local::get_internal_sparsity(
transpose, x_index, for_jac_sparsity, pack_r
);
local::get_internal_sparsity(
transpose, y_index, rev_hes_sparsity, pack_u
);
//
ok = rev_sparse_hes(vx, bool_s, bool_t, q, pack_r, pack_u, pack_v, x);
if( ! ok )
ok = rev_sparse_hes(vx, bool_s, bool_t, q, pack_r, pack_u, pack_v);
if( ! ok )
{ msg = afun_name() + msg + " sparsity = pack_sparsity_enum";
CPPAD_ASSERT_KNOWN(false, msg.c_str());
}
local::set_internal_sparsity(zero_empty, input_empty,
transpose, x_index, rev_hes_sparsity, pack_v
);
}
else if( sparsity_ == bool_sparsity_enum )
{ vector<bool>& bool_r( work_[thread]->bool_r );
vector<bool>& bool_u( work_[thread]->bool_u );
vector<bool>& bool_v( work_[thread]->bool_h );
//
bool_v.resize(n * q);
//
local::get_internal_sparsity(
transpose, x_index, for_jac_sparsity, bool_r
);
local::get_internal_sparsity(
transpose, y_index, rev_hes_sparsity, bool_u
);
//
ok = rev_sparse_hes(vx, bool_s, bool_t, q, bool_r, bool_u, bool_v, x);
if( ! ok )
ok = rev_sparse_hes(vx, bool_s, bool_t, q, bool_r, bool_u, bool_v);
if( ! ok )
{ msg = afun_name() + msg + " sparsity = bool_sparsity_enum";
CPPAD_ASSERT_KNOWN(false, msg.c_str());
}
local::set_internal_sparsity(zero_empty, input_empty,
transpose, x_index, rev_hes_sparsity, bool_v
);
}
else
{ CPPAD_ASSERT_UNKNOWN( sparsity_ == set_sparsity_enum );
vector< std::set<size_t> >& set_r( work_[thread]->set_r );
vector< std::set<size_t> >& set_u( work_[thread]->set_u );
vector< std::set<size_t> >& set_v( work_[thread]->set_h );
//
set_v.resize(n);
//
local::get_internal_sparsity(
transpose, x_index, for_jac_sparsity, set_r
);
local::get_internal_sparsity(
transpose, y_index, rev_hes_sparsity, set_u
);
//
ok = rev_sparse_hes(vx, bool_s, bool_t, q, set_r, set_u, set_v, x);
if( ! ok )
ok = rev_sparse_hes(vx, bool_s, bool_t, q, set_r, set_u, set_v);
if( ! ok )
{ msg = afun_name() + msg + " sparsity = set_sparsity_enum";
CPPAD_ASSERT_KNOWN(false, msg.c_str());
}
local::set_internal_sparsity(zero_empty, input_empty,
transpose, x_index, rev_hes_sparsity, set_v
);
}
for(size_t j = 0; j < n; j++)
{ if( x_index[j] > 0 )
rev_jac_flag[ x_index[j] ] |= bool_t[j];
}
return ok;
}
