{"ID":2859678,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04467","arxiv_id":"2510.04467","title":"A Time-certified Predictor-corrector IPM Algorithm for Box-QP","abstract":"Minimizing both the worst-case and average execution times of optimization algorithms is equally critical in real-time optimization-based control applications such as model predictive control (MPC). Most MPC solvers have to trade off between certified worst-case and practical average execution times. For example, our previous work [1] proposed a full-Newton path-following interior-point method (IPM) with data-independent, simple-calculated, and exact $O(\\sqrt{n})$ iteration complexity, but not as efficient as the heuristic Mehrotra predictor-corrector IPM algorithm (which sacrifices global convergence). This letter proposes a new predictor-corrector IPM algorithm that preserves the same certified $O(\\sqrt{n})$ iteration complexity while achieving a $5\\times$ speedup over [1]. Numerical experiments and codes that validate these results are provided.","short_abstract":"Minimizing both the worst-case and average execution times of optimization algorithms is equally critical in real-time optimization-based control applications such as model predictive control (MPC). Most MPC solvers have to trade off between certified worst-case and practical average execution times. For example, our p...","url_abs":"https://arxiv.org/abs/2510.04467","url_pdf":"https://arxiv.org/pdf/2510.04467v1","authors":"[\"Liang Wu\",\"Yunhong Che\",\"Richard D. Braatz\",\"Jan Drgona\"]","published":"2025-10-06T03:42:28Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}