Composite optimization with coupling constraints via dual proximal gradient method with applications to asynchronous networks

Abstract

AbstractIn this article, we consider solving a composite optimization problem with affine coupling constraints in a multi‐agent network based on proximal gradient method. In this problem, all the agents jointly minimize the sum of individual cost functions composed of smooth and possibly non‐smooth parts. To this end, we derive the dual problem by the concept of Fenchel conjugate, which gives rise to the dual proximal gradient (DPG) algorithm by allowing for the asymmetric individual interpretations of the coupling constraints. Then, an asynchronous DPG (Asyn‐DPG) algorithm is proposed for the asynchronous networks with heterogeneous step‐sizes and communication delays. For both the two algorithms, if the non‐smooth parts of the objective functions are simple‐structured, we only need to update dual variables by some simple operations, accounting for the reduction of the overall computational complexity. Analytical convergence rate of the proposed algorithms is derived and their efficacy is verified by solving a social welfare optimization problem of electricity market in the numerical simulation.