On approximations of stochastic optimal control problems with an application to climate equations

Fuente: arXiv
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Main Authors: Flandoli, Franco, Guatteri, Giuseppina, Pappalettera, Umberto, Tessitore, Gianmario
Format: Preprint
Published: 2024
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author Flandoli, Franco
Guatteri, Giuseppina
Pappalettera, Umberto
Tessitore, Gianmario
author_facet Flandoli, Franco
Guatteri, Giuseppina
Pappalettera, Umberto
Tessitore, Gianmario
contents The paper is devoted to the optimal control of a system with two time-scales, in a regime when the limit equation is not of averaging type but, in the spirit of Wong-Zakai principle, it is a stochastic differential equation for the slow variable, with noise emerging from the fast one. It proves that it is possible to control the slow variable by acting only on the fast scales. The concrete problem, of interest for climate research, is embedded into an abstract framework in Hilbert spaces, with a stochastic process driven by an approximation of a given noise. The principle established here is that convergence of the uncontrolled problem is sufficient for convergence of both the optimal costs and the optimal controls. This target is reached using Girsanov transform and the representation of the optimal cost and the optimal controls using a Forward Backward System. A challenge in this program is represented by the generality considered here of unbounded control actions.
format Preprint
id arxiv_https___arxiv_org_abs_2411_16491
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle On approximations of stochastic optimal control problems with an application to climate equations
Flandoli, Franco
Guatteri, Giuseppina
Pappalettera, Umberto
Tessitore, Gianmario
Optimization and Control
93E20, 60H15(Primary) 93E03 (Secondary)
The paper is devoted to the optimal control of a system with two time-scales, in a regime when the limit equation is not of averaging type but, in the spirit of Wong-Zakai principle, it is a stochastic differential equation for the slow variable, with noise emerging from the fast one. It proves that it is possible to control the slow variable by acting only on the fast scales. The concrete problem, of interest for climate research, is embedded into an abstract framework in Hilbert spaces, with a stochastic process driven by an approximation of a given noise. The principle established here is that convergence of the uncontrolled problem is sufficient for convergence of both the optimal costs and the optimal controls. This target is reached using Girsanov transform and the representation of the optimal cost and the optimal controls using a Forward Backward System. A challenge in this program is represented by the generality considered here of unbounded control actions.
title On approximations of stochastic optimal control problems with an application to climate equations
topic Optimization and Control
93E20, 60H15(Primary) 93E03 (Secondary)
url https://arxiv.org/abs/2411.16491