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Title: Small-disturbance input-to-state stability of perturbed gradient flows: Applications to LQR problem
This paper studies the effect of perturbations on the gradient flow of a general nonlinear programming problem, where the perturbation may arise from inaccurate gradient estimation in the setting of data-driven optimization. Under suitable conditions on the objective function, the perturbed gradient flow is shown to be small-disturbance input-to-state stable (ISS), which implies that, in the presence of a small-enough perturbation, the trajectories of the perturbed gradient flow must eventually enter a small neighborhood of the optimum. This work was motivated by the question of robustness of direct methods for the linear quadratic regulator problem, and specifically the analysis of the effect of perturbations caused by gradient estimation or round-off errors in policy optimization. We show small-disturbance ISS for three of the most common optimization algorithms: standard gradient flow, natural gradient flow, and Newton gradient flow.  more » « less
Award ID(s):
2210320 2227153
PAR ID:
10513252
Author(s) / Creator(s):
; ;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Systems & Control Letters
Volume:
188
Issue:
C
ISSN:
0167-6911
Page Range / eLocation ID:
105804
Subject(s) / Keyword(s):
Input-to-state stability (ISS) Learning-based control Policy optimization Gradient systems Linear quadratic regulator (LQR)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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