VectorBase ADFun<Base>::Reverse(size_t q, const VectorBase &w)
{ // constants
const Base zero(0);
// temporary indices
size_t i, j, k;
// number of independent variables
size_t n = ind_taddr_.size();
// number of dependent variables
size_t m = dep_taddr_.size();
pod_vector<Base> Partial;
Partial.extend(num_var_tape_ * q);
// update maximum memory requirement
// memoryMax = std::max( memoryMax,
// Memory() + num_var_tape_ * q * sizeof(Base)
// );
// check VectorBase is Simple Vector class with Base type elements
CheckSimpleVector<Base, VectorBase>();
CPPAD_ASSERT_KNOWN(
size_t(w.size()) == m || size_t(w.size()) == (m * q),
"Argument w to Reverse does not have length equal to\n"
"the dimension of the range for the corresponding ADFun."
);
CPPAD_ASSERT_KNOWN(
q > 0,
"The first argument to Reverse must be greater than zero."
);
CPPAD_ASSERT_KNOWN(
num_order_taylor_ >= q,
"Less that q taylor_ coefficients are currently stored"
" in this ADFun object."
);
// special case where multiple forward directions have been computed,
// but we are only using the one direction zero order results
if( (q == 1) & (num_direction_taylor_ > 1) )
{ num_order_taylor_ = 1; // number of orders to copy
size_t c = cap_order_taylor_; // keep the same capacity setting
size_t r = 1; // only keep one direction
capacity_order(c, r);
}
CPPAD_ASSERT_KNOWN(
num_direction_taylor_ == 1,
"Reverse mode for Forward(q, r, xq) with more than one direction"
"\n(r > 1) is not yet supported for q > 1."
);
// initialize entire Partial matrix to zero
for(i = 0; i < num_var_tape_; i++)
for(j = 0; j < q; j++)
Partial[i * q + j] = zero;
// set the dependent variable direction
// (use += because two dependent variables can point to same location)
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr_[i] < num_var_tape_ );
if( size_t(w.size()) == m )
Partial[dep_taddr_[i] * q + q - 1] += w[i];
else
{ for(k = 0; k < q; k++)
// ? should use += here, first make test to demonstrate bug
Partial[ dep_taddr_[i] * q + k ] = w[i * q + k ];
}
}
// evaluate the derivatives
CPPAD_ASSERT_UNKNOWN( cskip_op_.size() == play_.num_op_rec() );
CPPAD_ASSERT_UNKNOWN( load_op_.size() == play_.num_load_op_rec() );
ReverseSweep(
q - 1,
n,
num_var_tape_,
&play_,
cap_order_taylor_,
taylor_.data(),
q,
Partial.data(),
cskip_op_.data(),
load_op_
);
// return the derivative values
VectorBase value(n * q);
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr_[j] < num_var_tape_ );
// independent variable taddr equals its operator taddr
CPPAD_ASSERT_UNKNOWN( play_.GetOp( ind_taddr_[j] ) == InvOp );
// by the Reverse Identity Theorem
// partial of y^{(k)} w.r.t. u^{(0)} is equal to
// partial of y^{(q-1)} w.r.t. u^{(q - 1 - k)}
if( size_t(w.size()) == m )
{ for(k = 0; k < q; k++)
value[j * q + k ] =
Partial[ind_taddr_[j] * q + q - 1 - k];
}
else
{ for(k = 0; k < q; k++)
value[j * q + k ] =
Partial[ind_taddr_[j] * q + k];
}
}
CPPAD_ASSERT_KNOWN( ! ( hasnan(value) && check_for_nan_ ) ,
"dw = f.Reverse(q, w): has a nan,\n"
"but none of its Taylor coefficents are nan."
);
return value;
}
VectorBase ADFun<Base>::Reverse(size_t p, const VectorBase &w)
{ // constants
const Base zero(0);
// temporary indices
size_t i, j, k;
// number of independent variables
size_t n = ind_taddr_.size();
// number of dependent variables
size_t m = dep_taddr_.size();
pod_vector<Base> Partial;
Partial.extend(total_num_var_ * p);
// update maximum memory requirement
// memoryMax = std::max( memoryMax,
// Memory() + total_num_var_ * p * sizeof(Base)
// );
// check VectorBase is Simple Vector class with Base type elements
CheckSimpleVector<Base, VectorBase>();
CPPAD_ASSERT_KNOWN(
size_t(w.size()) == m || size_t(w.size()) == (m * p),
"Argument w to Reverse does not have length equal to\n"
"the dimension of the range for the corresponding ADFun."
);
CPPAD_ASSERT_KNOWN(
p > 0,
"The first argument to Reverse must be greater than zero."
);
CPPAD_ASSERT_KNOWN(
taylor_per_var_ >= p,
"Less that p taylor_ coefficients are currently stored"
" in this ADFun object."
);
// initialize entire Partial matrix to zero
for(i = 0; i < total_num_var_; i++)
for(j = 0; j < p; j++)
Partial[i * p + j] = zero;
// set the dependent variable direction
// (use += because two dependent variables can point to same location)
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr_[i] < total_num_var_ );
if( size_t(w.size()) == m )
Partial[dep_taddr_[i] * p + p - 1] += w[i];
else
{ for(k = 0; k < p; k++)
// ? should use += here, first make test to demonstrate bug
Partial[ dep_taddr_[i] * p + k ] = w[i * p + k ];
}
}
// evaluate the derivatives
ReverseSweep(
p - 1,
n,
total_num_var_,
&play_,
taylor_col_dim_,
taylor_.data(),
p,
Partial.data(),
cskip_op_
);
// return the derivative values
VectorBase value(n * p);
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr_[j] < total_num_var_ );
// independent variable taddr equals its operator taddr
CPPAD_ASSERT_UNKNOWN( play_.GetOp( ind_taddr_[j] ) == InvOp );
// by the Reverse Identity Theorem
// partial of y^{(k)} w.r.t. u^{(0)} is equal to
// partial of y^{(p-1)} w.r.t. u^{(p - 1 - k)}
if( size_t(w.size()) == m )
{ for(k = 0; k < p; k++)
value[j * p + k ] =
Partial[ind_taddr_[j] * p + p - 1 - k];
}
else
{ for(k = 0; k < p; k++)
value[j * p + k ] =
Partial[ind_taddr_[j] * p + k];
}
}
CPPAD_ASSERT_KNOWN( ! ( hasnan(value) && check_for_nan_ ) ,
"dw = f.Reverse(p, w): has a nan,\n"
"but none of its Taylor coefficents are nan."
);
return value;
}
VectorBase ADFun<Base>::Reverse(size_t p, const VectorBase &w) const
{ // temporary indices
size_t i, j, k;
// number of independent variables
size_t n = ind_taddr_.size();
// number of dependent variables
size_t m = dep_taddr_.size();
Base *Partial = CPPAD_NULL;
Partial = CPPAD_TRACK_NEW_VEC(total_num_var_ * p, Partial);
// update maximum memory requirement
// memoryMax = std::max( memoryMax,
// Memory() + total_num_var_ * p * sizeof(Base)
// );
// check VectorBase is Simple Vector class with Base type elements
CheckSimpleVector<Base, VectorBase>();
CPPAD_ASSERT_KNOWN(
w.size() == m,
"Argument w to Reverse does not have length equal to\n"
"the dimension of the range for the corresponding ADFun."
);
CPPAD_ASSERT_KNOWN(
p > 0,
"The first argument to Reverse must be greater than zero."
);
CPPAD_ASSERT_KNOWN(
taylor_per_var_ >= p,
"Less that p taylor_ coefficients are currently stored"
" in this ADFun object."
);
// initialize entire Partial matrix to zero
for(i = 0; i < total_num_var_; i++)
for(j = 0; j < p; j++)
Partial[i * p + j] = Base(0);
// set the dependent variable direction
// (use += because two dependent variables can point to same location)
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr_[i] < total_num_var_ );
Partial[dep_taddr_[i] * p + p - 1] += w[i];
}
// evaluate the derivatives
ReverseSweep(
p - 1,
total_num_var_,
&play_,
taylor_col_dim_,
taylor_,
p,
Partial
);
// return the derivative values
VectorBase value(n * p);
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr_[j] < total_num_var_ );
// independent variable taddr equals its operator taddr
CPPAD_ASSERT_UNKNOWN( play_.GetOp( ind_taddr_[j] ) == InvOp );
// by the Reverse Identity Theorem
// partial of y^{(k)} w.r.t. u^{(0)} is equal to
// partial of y^{(p-1)} w.r.t. u^{(p - 1 - k)}
for(k = 0; k < p; k++)
value[j * p + k ] =
Partial[ind_taddr_[j] * p + p - 1 - k];
}
// done with the Partial array
CPPAD_TRACK_DEL_VEC(Partial);
return value;
}
