49 : m_variables{detail::AdjointExpressionGraph{variable}
50 .generate_gradient_tree(wrt)},
52 slp_assert(m_wrt.cols() == 1);
55 for (
size_t col = 0; col < m_wrt.size(); ++col) {
56 m_wrt[col].expr->col = col;
59 for (
auto& variable : m_variables) {
60 m_graphs.emplace_back(variable);
64 for (
auto& node : m_wrt) {
68 for (
int row = 0; row < m_variables.rows(); ++row) {
69 if (m_variables[row].expr ==
nullptr) {
73 if (m_variables[row].type() == ExpressionType::LINEAR) {
77 m_graphs[row].append_gradient_triplets(m_cached_triplets, row, m_wrt);
78 }
else if (m_variables[row].type() > ExpressionType::LINEAR) {
81 m_nonlinear_rows.emplace_back(row);
85 if (m_nonlinear_rows.empty()) {
86 m_H.setFromTriplets(m_cached_triplets.begin(), m_cached_triplets.end());
87 if constexpr (UpLo == Eigen::Lower) {
88 m_H = m_H.triangularView<Eigen::Lower>();
124 const Eigen::SparseMatrix<double>&
value() {
125 if (m_nonlinear_rows.empty()) {
129 for (
auto& graph : m_graphs) {
130 graph.update_values();
135 auto triplets = m_cached_triplets;
138 for (
int row : m_nonlinear_rows) {
139 m_graphs[row].append_gradient_triplets(triplets, row, m_wrt);
142 m_H.setFromTriplets(triplets.begin(), triplets.end());
143 if constexpr (UpLo == Eigen::Lower) {
144 m_H = m_H.triangularView<Eigen::Lower>();