Xu Du, Jingzhe Wang
To be apper in 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{Du2025,
title={Distributed Consensus Optimization with Consensus {ALADIN}},
author={Xu Du and Jingzhe Wang},
booktitle = {American Control Conference},
year={2025 (accepted for publication)}
}