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This content will become publicly available on December 13, 2024

Title: On the Hardness of Learning to Stabilize Linear Systems
Inspired by the work of Tsiamis et al. [1], in this paper we study the statistical hardness of learning to stabilize linear time-invariant systems. Hardness is measured by the number of samples required to achieve a learning task with a given probability. The work in [1] shows that there exist system classes that are hard to learn to stabilize with the core reason being the hardness of identification. Here we present a class of systems that can be easy to identify, thanks to a non-degenerate noise process that excites all modes, but the sample complexity of stabilization still increases exponentially with the system dimension. We tie this result to the hardness of co-stabilizability for this class of systems using ideas from robust control.  more » « less
Award ID(s):
1931982
NSF-PAR ID:
10480624
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
IEEE Conference on Decision and Control (CDC)
Date Published:
Journal Name:
Proceedings of the IEEE Conference on Decision Control
ISSN:
0743-1546
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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