Paper Info
Title
Online Optimization In Dynamic Environments: A Regret Analysis For Sparse Problems
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.
Year
DOI
Venue
2018
10.1109/CDC.2018.8619583
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Online algorithm,Autoregressive model,Mathematical optimization,Regret,Online optimization,Recursion,Mathematics
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Sophie M. Fosson1448.96