This paper introduces HPIPM, a high-performance framework for quadratic programming (QP), designed to provide building blocks to efficiently and reliably solve model predictive control problems. HPIPM currently supports three QP types, and provides interior point method (IPM) solvers as well (partial) condensing routines. In particular, the IPM for optimal control QPs is intended to supersede the HPMPC solver, and it largely improves robustness while keeping the focus on speed. Numerical experiments show that HPIPM reliably solves challenging QPs, and that it outperforms other state-of-the-art solvers in speed.
@article{arxiv.2003.02547,
title = {HPIPM: a high-performance quadratic programming framework for model predictive control},
author = {Gianluca Frison and Moritz Diehl},
journal= {arXiv preprint arXiv:2003.02547},
year = {2020}
}