Distributed Proximal Algorithms for Nonsmooth Optimization: Unified Convergence Analysis

Yi Huang, Shisheng Cui, Jian Sun, Ziyang Meng

Abstract: This article studies two classes of nonsmooth distributed optimization problems with coupled constraints, in which the local cost function of each agent consists of a Lipschitz differentiable function and a nonsmooth function. By applying the primal–dual method and proximal operation, we propose two discrete-time distributed algorithms to solve the nonsmooth resource allocation problem and optimal consensus problem, respectively. Different from some previous results with decreasing step-sizes, the proposed algorithms are developed by using the constant step-sizes, which achieves a faster convergence rate. Moreover, we find that these two distributed proximal algorithms have the same structure and can be formulated in a unified framework. A unified convergence analysis is shown that these two algorithms achieve exact convergence to an optimal solution with an ergodic convergence rate $O(1/k)$. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed algorithms.

External IDs:dblp:journals/tac/HuangCSM25