Xu Du, Jingzhe Wang
In Proceedings of the American Control Conference, Denver, Colorado, USA, 2025.
The paper proposes the Consensus Augmented Lagrange Alternating Direction Inexact Newton (Consensus ALADIN) algorithm, a novel approach for solving dis tributed consensus optimization problems (DC). Consensus ALADIN allows each agent to independently solve its own nonlinear programming problem while coordinating with other agents by solving a consensus quadratic program ming (QP) problem. Building on this, we propose Broyden-Fletcher-Goldfarb-Shanno (BFGS) Consensus ALADIN, a communication-and-computation-efficient Consensus ALADIN. BFGS Consensus ALADIN improves communication efficiency through BFGS approximation techniques and enhances compu tational efficiency by deriving a closed form for the consensus QP problem. Additionally, by replacing the BFGS approxi mation with a scaled identity matrix, we develop Reduced Consensus ALADIN, a more computationally efficient variant. We establish the convergence theory for Consensus ALADIN and demonstrate its effectiveness through application to a non convex sensor allocation problem.
@INPROCEEDINGS{Du2025ACC,
author={Du, X. and Wang, J.},
booktitle={2025 American Control Conference (ACC)},
title={Distributed Consensus Optimization with Consensus ALADIN},
year={2025},
pages={2949-2955},
}