Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
For distributed optimization in bandwidth-constrained networks, this work provides a communication-efficient algorithm with theoretical guarantees.
This paper addresses distributed resource allocation over directed graphs with limited bandwidth, proposing a distributed algorithm that integrates Proximal Jacobian ADMM with quantized consensus. It achieves sublinear convergence to a neighborhood of the optimal solution, with accuracy bounded by quantization level.
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.