On the Primal Feasibility in Dual Decomposition Methods Under Additive and Bounded Errors

TL;DR

This paper analyzes the convergence of dual decomposition methods in distributed optimization under additive and bounded errors, providing theoretical guarantees and practical insights into primal feasibility and optimality.

Contribution

It offers a systematic analysis of primal feasibility convergence in dual decomposition methods with inexact data, including convergence rates and neighborhood bounds.

Findings

Algorithms converge to a neighborhood of the optimal solution.

Convergence rate depends on the level of system distortions.

Theoretical results are validated through numerical experiments.

Abstract

With the unprecedented growth of signal processing and machine learning application domains, there has been a tremendous expansion of interest in distributed optimization methods to cope with the underlying large-scale problems. Nonetheless, inevitable system-specific challenges such as limited computational power, limited communication, latency requirements, measurement errors, and noises in wireless channels impose restrictions on the exactness of the underlying algorithms. Such restrictions have appealed to the exploration of algorithms' convergence behaviors under inexact settings. Despite the extensive research conducted in the area, it seems that the analysis of convergences of dual decomposition methods concerning primal optimality violations, together with dual optimality violations is less investigated. Here, we provide a systematic exposition of the convergence of feasible points in dual decomposition methods under inexact settings, for an important class of global consensus optimization problems. Convergences and the rate of convergences of the algorithms are mathematically substantiated, not only from a dual-domain standpoint but also from a primal-domain standpoint. Analytical results show that the algorithms converge to a neighborhood of optimality, the size of which depends on the level of underlying distortions. Further elaboration of a generalized problem formulation is also furnished, together with the convergence properties of the underlying algorithms. Finally, theoretical derivations are verified by numerical experiments.