This paper presents a fast and flexible projected primal-dual method for solving linear quadratic optimal control problems with box constraints. Using a specific preconditioning, the algorithm achieves dead-beat convergence for unconstrained problems and has fast convergence for constrained problems. Accelerated convergence is obtained by applying a heavy-ball method to accelerate the projected primal-dual algorithm. In order to avoid missing critical points due to high momentum, an adaptive restarting procedure is used to slow the algorithm down if the solution diverges. Furthermore, convergence is proven by representing the algorithm as a Lur'e-type dynamic system and applying LaSalle's invariance principle to show the fixed point is asymptotically stable. The resulting algorithm is simple, while also achieving competitive computational times.
Original languageEnglish
Number of pages6
Publication statusAccepted/In press - 2021
Event60th IEEE Conference on Decision and Control (CDC 2021) - Austin, United States
Duration: 13 Dec 202115 Dec 2021
Conference number: 60


Conference60th IEEE Conference on Decision and Control (CDC 2021)
Abbreviated titleCDC 2021
Country/TerritoryUnited States
Internet address


Dive into the research topics of 'An Adaptive Restart Heavy-Ball Projected Primal-Dual Method for Solving Constrained Linear Quadratic Optimal Control Problems'. Together they form a unique fingerprint.

Cite this