void Concat::propagateSparsity(DMatrixPtrV& input, DMatrixPtrV& output, bool fwd) {
bvec_t *res_ptr = get_bvec_t(output[0]->data());
for (int i=0; i<input.size(); ++i) {
vector<double>& arg_i = input[i]->data();
bvec_t *arg_i_ptr = get_bvec_t(arg_i);
if (fwd) {
copy(arg_i_ptr, arg_i_ptr+arg_i.size(), res_ptr);
res_ptr += arg_i.size();
} else {
for (int k=0; k<arg_i.size(); ++k) {
*arg_i_ptr++ |= *res_ptr;
*res_ptr++ = 0;
}
}
}
}
void EvaluationMX::propagateSparsity(DMatrixPtrV& arg, DMatrixPtrV& res,bool use_fwd) {
if (fcn_.spCanEvaluate(use_fwd)) {
// Propagating sparsity pattern supported
// Pass/clear forward seeds/adjoint sensitivities
for (int iind = 0; iind < fcn_.getNumInputs(); ++iind) {
// Input vector
vector<double> &v = fcn_.input(iind).data();
if (v.empty()) continue; // FIXME: remove?
if (arg[iind] == 0) {
// Set to zero if not used
fill_n(get_bvec_t(v), v.size(), bvec_t(0));
} else {
// Copy output
fcn_.input(iind).sparsity().set(
get_bvec_t(fcn_.input(iind).data()),
get_bvec_t(arg[iind]->data()),
arg[iind]->sparsity());
}
}
// Pass/clear adjoint seeds/forward sensitivities
for (int oind = 0; oind < fcn_.getNumOutputs(); ++oind) {
// Output vector
vector<double> &v = fcn_.output(oind).data();
if (v.empty()) continue; // FIXME: remove?
if (res[oind] == 0) {
// Set to zero if not used
fill_n(get_bvec_t(v), v.size(), bvec_t(0));
} else {
// Copy output
fcn_.output(oind).sparsity().set(
get_bvec_t(fcn_.output(oind).data()),
get_bvec_t(res[oind]->data()),
res[oind]->sparsity());
}
}
// Propagate seedsfcn_.
fcn_.spInit(use_fwd); // NOTE: should only be done once
fcn_.spEvaluate(use_fwd);
// Get the sensitivities
if (use_fwd) {
for (int oind = 0; oind < res.size(); ++oind) {
if (res[oind] != 0) {
res[oind]->sparsity().set(
get_bvec_t(res[oind]->data()),
get_bvec_t(fcn_.output(oind).data()),
fcn_.output(oind).sparsity());
}
}
} else {
for (int iind = 0; iind < arg.size(); ++iind) {
if (arg[iind] != 0) {
arg[iind]->sparsity().bor(
get_bvec_t(arg[iind]->data()),
get_bvec_t(fcn_.input(iind).data()),
fcn_.input(iind).sparsity());
}
}
}
// Clear seeds and sensitivities
for (int iind = 0; iind < arg.size(); ++iind) {
vector<double> &v = fcn_.input(iind).data();
fill(v.begin(), v.end(), 0);
}
for (int oind = 0; oind < res.size(); ++oind) {
vector<double> &v = fcn_.output(oind).data();
fill(v.begin(), v.end(), 0);
}
} else {
// Propagating sparsity pattern not supported
if (use_fwd) {
// Clear the outputs
for (int oind = 0; oind < res.size(); ++oind) {
// Skip of not used
if (res[oind] == 0)
continue;
// Get data array for output and clear it
bvec_t *outputd = get_bvec_t(res[oind]->data());
fill_n(outputd, res[oind]->size(), 0);
}
}
// Loop over inputs
for (int iind = 0; iind < arg.size(); ++iind) {
// Skip of not used
if (arg[iind] == 0)
continue;
// Skip if no seeds
if (use_fwd && arg[iind]->empty())
continue;
// Get data array for input
bvec_t *inputd = get_bvec_t(arg[iind]->data());
// Loop over outputs
for (int oind = 0; oind < res.size(); ++oind) {
// Skip of not used
if (res[oind] == 0)
continue;
// Skip if no seeds
if (!use_fwd && res[oind]->empty())
continue;
// Get the sparsity of the Jacobian block
CRSSparsity& sp = fcn_.jacSparsity(iind, oind, true);
if (sp.isNull() || sp.size() == 0)
continue; // Skip if zero
const int d1 = sp.size1();
//const int d2 = sp.size2();
const vector<int>& rowind = sp.rowind();
const vector<int>& col = sp.col();
// Get data array for output
bvec_t *outputd = get_bvec_t(res[oind]->data());
// Carry out the sparse matrix-vector multiplication
for (int i = 0; i < d1; ++i) {
for (int el = rowind[i]; el < rowind[i + 1]; ++el) {
// Get column
int j = col[el];
// Propagate dependencies
if (use_fwd) {
outputd[i] |= inputd[j];
} else {
inputd[j] |= outputd[i];
}
}
}
}
}
}
}
