PreprintCC BY-NC-ND 4.0arXiv
Discrete-time feedback linearization control for nonsmooth constrained optimization
Abstract
We propose a discrete-time feedback linearization control approach to solve nonsmooth linearly constrained composite optimization problems. By using proximal operators, we construct a dynamical system whose equilibria correspond to the stationary points of the optimization problem. Interpreting the Lagrange multipliers as control inputs, we employ feedback linearization control to steer the obtained dynamical system to a stable equilibrium. We analyze the convergence of the resulting closed-loop system both in the strongly convex setting and under the Polyak-Lojasiewicz condition. Finally, we illustrate the applicability of the approach through numerical experiments.
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BibTeX
@misc{cerone2026discretetime,
title = {{Discrete-time feedback linearization control for nonsmooth constrained optimization}},
author = {Vito Cerone and Sophie Marie Fosson and Simone Pirrera and Alice Re and Diego Regruto},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.29799},
eprint = {2609.29799},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.29799},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Discrete-time feedback linearization control for nonsmooth constrained optimization
AU - Vito Cerone
AU - Sophie Marie Fosson
AU - Simone Pirrera
AU - Alice Re
AU - Diego Regruto
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.29799
UR - https://arxiv.org/abs/2609.29799
AB - We propose a discrete-time feedback linearization control approach to solve nonsmooth linearly constrained composite optimization problems. By using proximal operators, we construct a dynamical system whose equilibria correspond to the stationary points of the optimization problem. Interpreting the Lagrange multipliers as control inputs, we employ feedback linearization control to steer the obtained dynamical system to a stable equilibrium. We analyze the convergence of the resulting closed-loop system both in the strongly convex setting and under the Polyak-Lojasiewicz condition. Finally, we illustrate the applicability of the approach through numerical experiments.
ER -