Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization

TL;DR

This paper introduces a unified framework based on Successive Convex Approximation techniques for parallel and distributed solutions to large-scale non-convex optimization problems, addressing challenges in engineering applications.

Contribution

It develops a general, flexible, and unified algorithmic framework that extends existing SCA methods for parallel and distributed implementation in large-scale non-convex optimization.

Findings

Unifies and generalizes existing SCA methods.

Provides a flexible approach with customizable function approximants.

Enhances efficiency through control of computation and communication.

Abstract

Recent years have witnessed a surge of interest in parallel and distributed optimization methods for large-scale systems. In particular, nonconvex large-scale optimization problems have found a wide range of applications in several engineering fields. The design and the analysis of such complex, large-scale, systems pose several challenges and call for the development of new optimization models and algorithms. The major contribution of this paper is to put forth a general, unified, algorithmic framework, based on Successive Convex Approximation (SCA) techniques, for the parallel and distributed solution of a general class of non-convex constrained (non-separable, networked) problems. The presented framework unifies and generalizes several existing SCA methods, making them appealing for a parallel/distributed implementation while offering a flexible selection of function approximants, step size schedules, and control of the computation/communication efficiency. This paper is organized according to the lectures that one of the authors delivered at the CIME Summer School on Centralized and Distributed Multi-agent Optimization Models and Algorithms, held in Cetraro, Italy, June 23--27, 2014. These lectures are: I) Successive Convex Approximation Methods: Basics; II) Parallel Successive Convex Approximation Methods; and III) Distributed Successive Convex Approximation Methods.