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math.OC2026
Reinforcement learning for adaptive interior point methods in convex quadratic programming
Jeremy Bertoncini, Alberto De Marchi, Matthias Gerdts +1
Quadratic programming is a workhorse of modern nonlinear optimization, control, and data science. Although regularized methods offer convergence guarantees under minimal assumption…
math.OC2025
Hybrid optimal control with mixed-integer Lagrangian methods
Viktoriya Nikitina, Alberto De Marchi, Matthias Gerdts
Models involving hybrid systems are versatile in their application but difficult to optimize efficiently due to their combinatorial nature. This work presents a method to cope with…
math.OC2024
Collision Avoidance using Iterative Dynamic and Nonlinear Programming with Adaptive Grid Refinements
Rebecca Richter, Alberto De Marchi, Matthias Gerdts
Nonlinear optimal control problems for trajectory planning with obstacle avoidance present several challenges. While general-purpose optimizers and dynamic programming methods stru…