void Optimizer::augment_sparse_linear_system(SparseSystem& W,
const Properties& prop) {
if (prop.method == DOG_LEG) {
// We're using the incremental version of Powell's Dog-Leg, so we need
// to form the updated gradient.
const VectorXd& f_new = W.rhs();
// Augment the running count for the sum-of-squared errors at the current
// linearization point.
current_SSE_at_linpoint += f_new.squaredNorm();
// Allocate the new gradient vector
VectorXd g_new(W.num_cols());
// Compute W^T \cdot f_new
VectorXd increment = mul_SparseMatrixTrans_Vector(W, f_new);
// Set g_new = (g_old 0)^T + W^T f_new.
g_new.head(gradient.size()) = gradient + increment.head(gradient.size());
g_new.tail(W.num_cols() - gradient.size()) = increment.tail(
W.num_cols() - gradient.size());
// Cache the new gradient vector
gradient = g_new;
}
// Apply Givens to QR factorize the newly augmented sparse system.
for (int i = 0; i < W.num_rows(); i++) {
SparseVector new_row = W.get_row(i);
function_system._R.add_row_givens(new_row, W.rhs()(i));
}
}
