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Soft inequality constraints in gradient method and fast gradient method for quadratic programming

  • Matija Perne
  • Samo Gerkšič
  • Boštjan Pregelj
Research Article
  • 64 Downloads

Abstract

A quadratic program (QP) with soft inequality constraints with both linear and quadratic costs on constraint violation can be solved with the dual gradient method (GM) or the dual fast gradient method (FGM). The treatment of the constraint violation influences the efficiency and usefulness of the algorithm. We improve on the classical way of extending the QP: our novel contribution is that we obtain the solution to the soft-constrained QP without explicitly introducing slack variables. This approach is more efficient than solving the extended QP with GM or FGM and results in a similar algorithm than if the soft constraints were replaced with hard ones. The approach is intended for applications in model predictive control with fast system dynamics, where QPs of this type are solved at every sampling time in the millisecond range.

Keywords

Model predictive control Quadratic programming First-order methods Soft constraints KKT optimality conditions 

Mathematics Subject Classification

90C20 49N05 93C05 65K10 49K20 

Notes

Acknowledgements

Research supported by Slovenian Research Agency (P2-0001). This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. The authors are grateful to the referees and Ðani Juričić for their helpful comments and useful suggestions that led to improvements in the manuscript.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Jožef Stefan InstituteLjubljanaSlovenia

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