Back to Search
Start Over
Complexity Certification of Proximal-Point Methods for Numerically Stable Quadratic Programming
- Publication Year :
- 2021
-
Abstract
- When solving a quadratic program (QP), one can improve the numerical stability of any QP solver by performing proximal-point outer iterations, resulting in solving a sequence of better conditioned QPs. In this paper we present a method which, for a given multi-parametric quadratic program (mpQP) and any polyhedral set of parameters, determines which sequences of QPs will have to be solved when using outer proximal-point iterations. By knowing this sequence, bounds on the worst-case complexity of the method can be obtained, which is of importance in, for example, real-time model predictive control (MPC) applications. Moreover, we combine the proposed method with previous work on complexity certification for active-set methods to obtain a more detailed certification of the proximal-point methods complexity, namely the total number of inner iterations.<br />Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]
Details
- Database :
- OAIster
- Notes :
- Arnstròˆm, Daniel, Bemporad, Alberto, Axehill, Daniel
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1312838377
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.23919.ACC50511.2021.9483329