Distributed Discrete-time Optimization with Coupling Constraints Based on Dual Proximal Gradient Method in Multi-agent Networks

In this paper, we aim to solve a distributed optimization problem with coupling constraints based on proximal gradient method in a multi-agent network, where the cost function of the agents is composed of smooth and possibly non-smooth parts. To solve this problem, we resort to the dual problem by deriving the Fenchel conjugate, resulting in a consensus based constrained optimization problem. Then, we propose a fully distributed dual proximal gradient algorithm, where the agents make decisions only with local parameters and the information of immediate neighbours. Moreover, provided that the non-smooth parts in the primal cost functions are with some simple structures, we only need to update dual variables by some simple operations and the overall computational complexity can be reduced. Analytical convergence rate of the proposed algorithm is derived and the efficacy is numerically verified by a social welfare optimization problem in the electricity market.