Controlling the level of sparsity in MPC

TL;DR

This paper introduces a flexible family of formulations for solving linear systems in MPC that allows tuning sparsity levels, leading to potentially better computational performance than traditional sparse or dense approaches.

Contribution

It demonstrates that a continuum of formulations with varying sparsity can be created, enabling optimized performance tailored to specific hardware and software environments.

Findings

A family of formulations with adjustable sparsity levels is feasible.

Optimal sparsity level can improve computational efficiency.

Classical sparse or dense formulations are not always optimal.

Abstract

In optimization routines used for on-line Model Predictive Control (MPC), linear systems of equations are usually solved in each iteration. This is true both for Active Set (AS) methods as well as for Interior Point (IP) methods, and for linear MPC as well as for nonlinear MPC and hybrid MPC. The main computational effort is spent while solving these linear systems of equations, and hence, it is of greatest interest to solve them efficiently. Classically, the optimization problem has been formulated in either of two different ways. One of them leading to a sparse linear system of equations involving relatively many variables to solve in each iteration and the other one leading to a dense linear system of equations involving relatively few variables. In this work, it is shown that it is possible not only to consider these two distinct choices of formulations. Instead it is shown that it is possible to create an entire family of formulations with different levels of sparsity and number of variables, and that this extra degree of freedom can be exploited to get even better performance with the software and hardware at hand. This result also provides a better answer to an often discussed question in MPC; should the sparse or dense formulation be used. In this work, it is shown that the answer to this question is that often none of these classical choices is the best choice, and that a better choice with a different level of sparsity actually can be found.