📝 SummarXiv

Abstract

Within the framework of the augmented Lagrangian (AL), we propose a novel distributed optimization method, termed Distributed Augmented Lagrangian Decomposition (DALD), and provide a rigorous convergence proof for its standard version. To address the high iteration costs in early stages, we propose several accelerated variants of DALD that enhances efficiency without compromising theoretical guarantees, supported by a comprehensive convergence analysis. To facilitate the description of the distributed optimization process, the concept of hierarchical coordination networks is introduced, integrating hierarchical matrix concepts to aid in this explanation. We further explore and expand the applicability of the DALD method and demonstrate how it unifies existing distributed optimization theories within the AL framework. The effectiveness and applicability of the proposed distributed optimization method and its variants are further validated through numerical experiments.