Inexact fast alternating minimization algorithm for distributed model predictive control

Abstract

This paper presents a new distributed optimization technique, the inexact fast alternating minimization algorithm (inexact FAMA), that allows for inexact local computation as well as for errors resulting from limited communication. We show that inexact FAMA is equivalent to the inexact accelerated proximal-gradient method applied to the dual problem and derive an upper-bound on the number of iterations for convergence for inexact FAMA. The second contribution of this work is that a weakened assumption for FAMA, as well as for its inexact version, is presented. The new assumption allows the strongly convex objective in the optimization problem to be subject to convex constraints, while still ..