Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918450588286976 |
|---|---|
| author | Du, Xu Han, Boyu Notarnicola, Ivano Johansson, Karl H. Rikos, Apostolos I. |
| author_facet | Du, Xu Han, Boyu Notarnicola, Ivano Johansson, Karl H. Rikos, Apostolos I. |
| contents | This paper investigates distributed resource allocation optimization over directed graphs with limited communication bandwidth. We develop a novel distributed algorithm that integrates the centralized Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) with a finite-level quantized consensus scheme, enabling nodes to cooperatively solve the optimization in a distributed fashion. Under the assumption of convex objective functions, we establish that the proposed algorithm achieves sublinear convergence to a neighborhood of the optimal solution, with the convergence accuracy explicitly bounded by the quantization level. Numerical experiments validate that the algorithm achieves competitive performance compared to existing approaches while exhibiting communication efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14861 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication Du, Xu Han, Boyu Notarnicola, Ivano Johansson, Karl H. Rikos, Apostolos I. Optimization and Control Systems and Control This paper investigates distributed resource allocation optimization over directed graphs with limited communication bandwidth. We develop a novel distributed algorithm that integrates the centralized Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) with a finite-level quantized consensus scheme, enabling nodes to cooperatively solve the optimization in a distributed fashion. Under the assumption of convex objective functions, we establish that the proposed algorithm achieves sublinear convergence to a neighborhood of the optimal solution, with the convergence accuracy explicitly bounded by the quantization level. Numerical experiments validate that the algorithm achieves competitive performance compared to existing approaches while exhibiting communication efficiency. |
| title | Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2604.14861 |