Exploiting Non-Negativity in DAG Structure Learning
The work addresses learning directed acyclic graphs (DAGs) from nodal observations generated by a linear structural equation model. By exploiting non-negative edge weights, the authors simplify acyclicity characterization and develop an augmented Lagrangian algorithm. This method proves that the true DAG is the unique global minimizer of the objective function under standard identifiability assumptions, outperforming existing continuous learning methods.