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arXiv · 2607.11835

Stabilize-then-optimize: Feedback transformations as preconditioners in optimal control

Till Preuster·Manuel Schaller·Anton Schiela·Martin Stoll
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Abstract

Many numerical algorithms for optimal control leverage an elimination of the state via the control-to-state map such as condensed approaches or preconditioned conjugate gradient methods for the optimality system. As such, the norm of the control-to-state map directly enters the convergence estimates for these methods, e.g., via the condition number of the associated linear system. In this work we show that using feedback transformations one may reformulate the optimal control problem to decrease the norm of the (feedbacked) control-to-state map, leading to a drastic improvement of the involved condition numbers. We illustrate the abstract approach for ordinary and partial differential equations such as parabolic, hyperbolic or elliptic equations. For each of these problem classes we provide a constructive method to improve solution operator norms via feedbacks. Further, we showcase the efficacy of the method by means of various numerical examples with elliptic, parabolic and hyperbolic partial differential equations.

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Till Preuster, Manuel Schaller, Anton Schiela, Martin Stoll. 2026-07-13. Stabilize-then-optimize: Feedback transformations as preconditioners in optimal control. https://arxiv.org/abs/2607.11835

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