Time-varying systems are a challenge in many scientific and engineering areas. Usually, estimation of time-varying parameters or signals must be performed online, which calls for the development of responsive online algorithms. In this paper, we consider this problem in the context of the sparse optimization; specifically, we consider the Elastic-net model. Following the rationale in [1], we propose a novel online algorithm and we theoretically prove that it is successful in terms of dynamic regret. We then show an application to recursive identification of time-varying autoregressive models, in the case when the number of parameters to be estimated is unknown. Numerical results show the practical efficiency of the proposed method.

Online Optimization in Dynamic Environments: A Regret Analysis for Sparse Problems / Fosson, Sophie M.. - (2018), pp. 7225-7230. (Intervento presentato al convegno IEEE CONFERENCE ON DECISION AND CONTROL (CDC) tenutosi a Miami, FL, USA nel Dicembre 2018) [10.1109/CDC.2018.8619583].

Online Optimization in Dynamic Environments: A Regret Analysis for Sparse Problems

Fosson, Sophie M.
2018

Abstract

Time-varying systems are a challenge in many scientific and engineering areas. Usually, estimation of time-varying parameters or signals must be performed online, which calls for the development of responsive online algorithms. In this paper, we consider this problem in the context of the sparse optimization; specifically, we consider the Elastic-net model. Following the rationale in [1], we propose a novel online algorithm and we theoretically prove that it is successful in terms of dynamic regret. We then show an application to recursive identification of time-varying autoregressive models, in the case when the number of parameters to be estimated is unknown. Numerical results show the practical efficiency of the proposed method.
2018
978-1-5386-1395-5
File in questo prodotto:
File Dimensione Formato  
cdc2018.pdf

accesso aperto

Descrizione: Articolo
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 376.8 kB
Formato Adobe PDF
376.8 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2729891
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo