VectorBase ADFun<Base>::SparseHessian(
const VectorBase& x, const VectorBase& w, const VectorSet& p
)
{ size_t i, j, k;
size_t n = Domain();
VectorBase hes(n * n);
CPPAD_ASSERT_KNOWN(
size_t(x.size()) == n,
"SparseHessian: size of x not equal domain size for f."
);
typedef typename VectorSet::value_type Set_type;
typedef typename internal_sparsity<Set_type>::pattern_type Pattern_type;
// initialize the return value as zero
Base zero(0);
for(i = 0; i < n; i++)
for(j = 0; j < n; j++)
hes[i * n + j] = zero;
// arguments to SparseHessianCompute
Pattern_type s;
CppAD::vector<size_t> row;
CppAD::vector<size_t> col;
sparse_hessian_work work;
bool transpose = false;
sparsity_user2internal(s, p, n, n, transpose);
k = 0;
for(i = 0; i < n; i++)
{ s.begin(i);
j = s.next_element();
while( j != s.end() )
{ row.push_back(i);
col.push_back(j);
k++;
j = s.next_element();
}
}
size_t K = k;
VectorBase H(K);
// now we have folded this into the following case
SparseHessianCompute(x, w, s, row, col, H, work);
// now set the non-zero return values
for(k = 0; k < K; k++)
hes[ row[k] * n + col[k] ] = H[k];
return hes;
}
// -----------------------------------------------------------------------
// get the result of the work
bool multi_newton_combine(CppAD::vector<double>& xout)
{ // number of threads in the calculation
size_t num_threads = std::max(num_threads_, size_t(1));
// remove duplicates and points that are not solutions
xout.resize(0);
bool ok = true;
size_t thread_num;
// initialize as more that sub_lenght_ / 2 from any possible solution
double xlast = - sub_length_;
for(thread_num = 0; thread_num < num_threads; thread_num++)
{ vector<double>& x = work_all_[thread_num]->x;
size_t i;
for(i = 0; i < x.size(); i++)
{ // check for case where this point is lower limit for this
// thread and upper limit for previous thread
if( fabs(x[i] - xlast) >= sub_length_ )
{ xout.push_back( x[i] );
xlast = x[i];
}
else
{ double fcur, flast, df;
fun_(x[i], fcur, df);
fun_(xlast, flast, df);
if( fabs(fcur) < fabs(flast) )
{ xout[ xout.size() - 1] = x[i];
xlast = x[i];
}
}
}
ok &= work_all_[thread_num]->ok;
}
// go down so free memory for other threads before memory for master
thread_num = num_threads;
while(thread_num--)
{
# if USE_THREAD_ALLOC_FOR_WORK_ALL
// call the destructor for CppAD::vector destructor
work_all_[thread_num]->x.~vector<double>();
// delete the raw memory allocation
void* v_ptr = static_cast<void*>( work_all_[thread_num] );
thread_alloc::return_memory( v_ptr );
# else
delete work_all_[thread_num];
# endif
// Note that xout corresponds to memroy that is inuse by master
// (so we can only chech have freed all their memory).
if( thread_num > 0 )
{ // check that there is no longer any memory inuse by this thread
ok &= thread_alloc::inuse(thread_num) == 0;
// return all memory being held for future use by this thread
thread_alloc::free_available(thread_num);
}
}
// now we are done with the work_all_ vector so free its memory
// (becasue it is a static variable)
work_all_.clear();
return ok;
}
VectorBase ADFun<Base>::SparseJacobian(
const VectorBase& x, const VectorSet& p
)
{ size_t i, j, k;
size_t m = Range();
size_t n = Domain();
VectorBase jac(m * n);
CPPAD_ASSERT_KNOWN(
size_t(x.size()) == n,
"SparseJacobian: size of x not equal domain size for f."
);
CheckSimpleVector<Base, VectorBase>();
typedef typename VectorSet::value_type Set_type;
typedef typename internal_sparsity<Set_type>::pattern_type Pattern_type;
// initialize the return value as zero
Base zero(0);
for(i = 0; i < m; i++)
for(j = 0; j < n; j++)
jac[i * n + j] = zero;
sparse_jacobian_work work;
CppAD::vector<size_t> row;
CppAD::vector<size_t> col;
if( n <= m )
{
// need an internal copy of sparsity pattern
Pattern_type s_transpose;
bool transpose = true;
sparsity_user2internal(s_transpose, p, m, n, transpose);
k = 0;
for(j = 0; j < n; j++)
{ s_transpose.begin(j);
i = s_transpose.next_element();
while( i != s_transpose.end() )
{ row.push_back(i);
col.push_back(j);
k++;
i = s_transpose.next_element();
}
}
size_t K = k;
VectorBase J(K);
// now we have folded this into the following case
SparseJacobianFor(x, s_transpose, row, col, J, work);
// now set the non-zero return values
for(k = 0; k < K; k++)
jac[ row[k] * n + col[k] ] = J[k];
}
else
{
// need an internal copy of sparsity pattern
Pattern_type s;
bool transpose = false;
sparsity_user2internal(s, p, m, n, transpose);
k = 0;
for(i = 0; i < m; i++)
{ s.begin(i);
j = s.next_element();
while( j != s.end() )
{ row.push_back(i);
col.push_back(j);
k++;
j = s.next_element();
}
}
size_t K = k;
VectorBase J(K);
// now we have folded this into the following case
SparseJacobianRev(x, s, row, col, J, work);
// now set the non-zero return values
for(k = 0; k < K; k++)
jac[ row[k] * n + col[k] ] = J[k];
}
return jac;
}
