A novel distributed proximal Jacobian ADMM algorithm integrated with quantized consensus achieves sublinear convergence to a neighborhood of the optimum for convex resource allocation over directed graphs, with error bounded by the quantization level.
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Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
A novel distributed proximal Jacobian ADMM algorithm integrated with quantized consensus achieves sublinear convergence to a neighborhood of the optimum for convex resource allocation over directed graphs, with error bounded by the quantization level.