Inexact Alternating Minimization Algorithm for Distributed Optimization with an Application to Distributed MPC

TL;DR

This paper introduces inexact alternating minimization algorithms for distributed optimization, providing convergence guarantees and iteration complexity bounds, with applications to distributed Model Predictive Control (MPC).

Contribution

It proposes inexact AMA and FAMA algorithms, establishes their equivalence to proximal-gradient methods, and applies them to distributed MPC with convergence analysis.

Findings

Derived iteration complexity bounds for inexact algorithms.

Established convergence conditions with local computational errors.

Applied methods to distributed MPC with guaranteed convergence.

Abstract

In this paper, we propose the inexact alternating minimization algorithm (inexact AMA), which allows inexact iterations in the algorithm, and its accelerated variant, called the inexact fast alternating minimization algorithm (inexact FAMA). We show that inexact AMA and inexact FAMA are equivalent to the inexact proximal-gradient method and its accelerated variant applied to the dual problem. Based on this equivalence, we derive complexity upper-bounds on the number of iterations for the inexact algorithms. We apply inexact AMA and inexact FAMA to distributed optimization problems, with an emphasis on distributed MPC applications, and show the convergence properties for this special case. By employing the complexity upper-bounds on the number of iterations, we provide sufficient conditions on the inexact iterations for the convergence of the algorithms. We further study the special case of quadratic local objectives in the distributed optimization problems, which is a standard form in distributed MPC. For this special case, we allow local computational errors at each iteration. By exploiting a warm-starting strategy and the sufficient conditions on the errors for convergence, we propose an approach to certify the number of iterations for solving local problems, which guarantees that the local computational errors satisfy the sufficient conditions and the inexact distributed optimization algorithm converges to the optimal solution.