56 : m_variables{std::move(variables)}, m_wrt{std::move(wrt)} {
57 slp_assert(m_variables.cols() == 1);
58 slp_assert(m_wrt.cols() == 1);
61 for (
size_t col = 0; col < m_wrt.size(); ++col) {
62 m_wrt[col].expr->col = col;
65 for (
auto& variable : m_variables) {
66 m_graphs.emplace_back(variable);
70 for (
auto& node : m_wrt) {
74 for (
int row = 0; row < m_variables.rows(); ++row) {
75 if (m_variables[row].expr ==
nullptr) {
79 if (m_variables[row].type() == ExpressionType::LINEAR) {
83 m_graphs[row].append_gradient_triplets(m_cached_triplets, row, m_wrt);
84 }
else if (m_variables[row].type() > ExpressionType::LINEAR) {
87 m_nonlinear_rows.emplace_back(row);
91 if (m_nonlinear_rows.empty()) {
92 m_J.setFromTriplets(m_cached_triplets.begin(), m_cached_triplets.end());
108 for (
int row = 0; row < m_variables.rows(); ++row) {
109 auto grad = m_graphs[row].generate_gradient_tree(m_wrt);
110 for (
int col = 0; col < m_wrt.rows(); ++col) {
111 if (grad[col].expr !=
nullptr) {
112 result[row, col] = std::move(grad[col]);
127 const Eigen::SparseMatrix<double>&
value() {
128 if (m_nonlinear_rows.empty()) {
132 for (
auto& graph : m_graphs) {
133 graph.update_values();
138 auto triplets = m_cached_triplets;
141 for (
int row : m_nonlinear_rows) {
142 m_graphs[row].append_gradient_triplets(triplets, row, m_wrt);
145 m_J.setFromTriplets(triplets.begin(), triplets.end());