New Primal-Dual Proximal Algorithm for Distributed Optimization

TL;DR

This paper introduces a new distributed primal-dual algorithm based on AFBA for networked agents to optimize private convex costs with linear convergence, improving efficiency and ease of implementation.

Contribution

It presents a novel AFBA-based primal-dual algorithm for distributed convex optimization, encompassing existing methods and offering linear convergence under certain conditions.

Findings

Algorithm achieves linear convergence for piecewise linear-quadratic costs.

Method is easy to implement without matrix inversions or inner loops.

Parameter tuning can significantly improve performance.

Abstract

We consider a network of agents, each with its own private cost consisting of the sum of two possibly nonsmooth convex functions, one of which is composed with a linear operator. At every iteration each agent performs local calculations and can only communicate with its neighbors. The goal is to minimize the aggregate of the private cost functions and reach a consensus over a graph. We propose a primal-dual algorithm based on \emph{Asymmetric Forward-Backward-Adjoint} (AFBA), a new operator splitting technique introduced recently by two of the authors. Our algorithm includes the method of Chambolle and Pock as a special case and has linear convergence rate when the cost functions are piecewise linear-quadratic. We show that our distributed algorithm is easy to implement without the need to perform matrix inversions or inner loops. We demonstrate through computational experiments how selecting the parameter of our algorithm can lead to larger step sizes and yield better performance.