2025/10/06 by Liang Wu, Yunhong Che, Wu, Liang +5
Engineering · Mathematics · #Advanced Control Systems Optimization #Advanced Optimization Algorithms Research #FOS: Mathematics #Optimization and Control (math.OC) #Stability and Control of Uncertain Systems
paper · pdf · doi:10.48550/arxiv.2510.04467
openalex publication_date 2025/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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(√(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(√(n)) iteration complexity while achieving a 5× speedup over [1]. Numerical experiments and codes that validate these results are provided.