The paper introduces a non-negative DAG learning formulation solved via method of multipliers, with proofs that the true DAG is the unique global minimizer and only acyclic KKT point in the population regime.
DAGs with No Fears: A closer look at continuous optimization for learning Bayesian networks,
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Exploiting Non-Negativity in DAG Structure Learning
The paper introduces a non-negative DAG learning formulation solved via method of multipliers, with proofs that the true DAG is the unique global minimizer and only acyclic KKT point in the population regime.