On the Convergence of Continuous Constrained Optimization for Structure Learning

Document Type

Article

Publication Title

arXiv

Abstract

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a continuous optimization problem by leveraging an algebraic characterization of acyclicity. The constrained problem is solved using the augmented Lagrangian method (ALM) which is often preferred to the quadratic penalty method (QPM) by virtue of its standard convergence result that does not require the penalty coefficient to go to infinity, hence avoiding ill-conditioning. However, the convergence properties of these methods for structure learning, including whether they are guaranteed to return a DAG solution, remain unclear, which might limit their practical applications. In this work, we examine the convergence of ALM and QPM for structure learning in the linear, nonlinear, and confounded cases. We show that the standard convergence result of ALM does not hold in these settings, and demonstrate empirically that its behavior is akin to that of the QPM which is prone to ill-conditioning. We further establish the convergence guarantee of QPM to a DAG solution, under mild conditions. Lastly, we connect our theoretical results with existing approaches to help resolve the convergence issue, and verify our findings in light of an empirical comparison of them. Copyright © 2020, The Authors. All rights reserved.

DOI

10.48550/arXiv.2011.11150

Publication Date

11-22-2020

Keywords

Constrained optimization, Lagrange multipliers, Machine learning, Structural optimization, Acyclicity, Augmented Lagrangian methods, Constrained problem, Continuous optimization problems, Convergence results, Ill-conditioning, Its standards, Penalty coefficient, Quadratic penalty methods, Structure-learning, Directed graphs, Machine Learning (cs.LG), Machine Learning (stat.ML)

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