Parallel and distributed optimization methods for estimation and control in networks

TL;DR

This paper reviews and analyzes parallel and distributed optimization methods for estimation and control in large-scale networked systems, highlighting their potential to improve system performance through joint optimization.

Contribution

It provides a systematic framework for formulating and applying parallel algorithms to complex networked systems, balancing convergence speed and communication.

Findings

Demonstrates how estimation and control problems can be formulated in parallel/distributed frameworks.

Shows tradeoffs between convergence speed, message passing, and computation architecture.

Includes applications illustrating the effectiveness of the approach.

Abstract

System performance for networks composed of interconnected subsystems can be increased if the traditionally separated subsystems are jointly optimized. Recently, parallel and distributed optimization methods have emerged as a powerful tool for solving estimation and control problems in large-scale networked systems. In this paper we review and analyze the optimization-theoretic concepts of parallel and distributed methods for solving coupled optimization problems and demonstrate how several estimation and control problems related to complex networked systems can be formulated in these settings. The paper presents a systematic framework for exploiting the potential of the decomposition structures as a way to obtain different parallel algorithms, each with a different tradeoff among convergence speed, message passing amount and distributed computation architecture. Several specific applications from estimation and process control are included to demonstrate the power of the approach.