Abstract:

Convex Quadratic Programs (QPs) have come to play a central role in the computation of control action for constrained dynamical systems. Convex QPs arise in Model Predictive Control (MPC) for linear systems, switched linear systems and in sequential linearization of nonlinear systems. A number algorithms have been developed in recent years for the solution of such QPs. However not all algorithms are capable of computing an optimal solution if feasible or producing a certificate of infeasibility. In this paper, we present a novel Homogeneous QP (HQP) formulation which is obtained by embedding the original QP in a larger space. The key properties of the HQP are: (i) is always feasible, (ii) an optimal solution to QP can be readily obtained from a solution to HQP, and (iii) infeasibility of QP corresponds to a particular solution of HQP. An immediate consequence is that all the existing algorithms for QP are now also capable of robustly detecting infeasibility. In particular, we present an Infeasible Interior Point Method (IIPM) for the HQP and show polynomial iteration complexity when applied to HQP. A key distinction with prior IPM approaches is that we do not need to solve second-order cone programs. Numerical experiments on the formulation are provided using existing codes.