Fast alternating minimization algorithm for model predictive control

Abstract

In this work, we apply the fast alternating minimization algorithm (FAMA) to model predictive control (MPC) problems with polytopic and second-order cone constraints. We present a splitting strategy, which speeds up FAMA by reducing each iteration to simple operations. We show that FAMA provides not only good performance for solving MPC problems when compared to other alternating direction methods, but also superior theoretical properties. Specifically, we derive complexity bounds on the number of iterations for both dual and primal variables, which are of particular relevance in the context of real-time MPC to bound the required online computation time. For MPC problems with polyhedral and ..