Lift, Partition, and Project: Parametric Complexity Certification of Active-Set QP Methods in the Presence of Numerical Errors

TL;DR

This paper introduces a framework to enhance parametric complexity certification methods for active-set quadratic programming solvers, explicitly accounting for numerical errors that can affect solver performance in real-time Model Predictive Control applications.

Contribution

It proposes a general framework that integrates numerical error considerations into existing complexity certification methods for active-set QP solvers.

Findings

Framework effectively accounts for numerical errors in complexity bounds

Applicable to any certification method for active-set QP solvers

Improves reliability of complexity estimates in real-time control scenarios

Abstract

When Model Predictive Control (MPC) is used in real-time to control linear systems, quadratic programs (QPs) need to be solved within a limited time frame. Recently, several parametric methods have been proposed that certify the number of computations active-set QP solvers require to solve these QPs. These certification methods, hence, ascertain that the optimization problem can be solved within the limited time frame. A shortcoming in these methods is, however, that they do not account for numerical errors that might occur internally in the solvers, which ultimately might lead to optimistic complexity bounds if, for example, the solvers are implemented in single precision. In this paper we propose a general framework that can be incorporated in any of these certification methods to account for such numerical errors.