Distributed Proximal-Correction Algorithm for the Sum of Maximal Monotone Operators in Multi-Agent Network

This paper focuses on a class of inclusion problems of maximal monotone operators in a multi-agent network, where each agent is characterized by an operator that is not available to any other agents, but the agents can cooperate by exchanging information with their neighbors according to a given communication topology. All agents aim at finding a common decision vector that is the solution to the sum of agents' operators. This class of problems is motivated by distributed convex optimization with coupled constraints. In this paper, we propose a distributed proximal point method with a cumulative correction term (named Proximal-Correction Algorithm) for this class of inclusion problems of operators. It's proved that the Proximal-Correction Algorithm converges for any value of a constant penalty parameter. In order to make the Proximal-Correction ALgorithm computationally implementable for a wide variety of distributed optimization problems, we adopt two inexact criteria for calculating the proximal steps of the algorithm. Under each of these two criteria, the convergence of Proximal-Correction Algorithm can be guaranteed, and the linear convergence rate is established when the stronger one is satisfied. In numerical simulations, both exact and inexact versions of Proximal-Correction Algorithm are executed for a distributed convex optimization problem with coupled constraints. Compared with several alternative algorithms in the literature, the exact and inexact versions of Proximal-Correction both exhibit good numerical performance