InforLorV4, Main, Merge, bibRecord, 000D84

A Difference of Convex Functions Algorithm for Switched Linear Regression

Identifieur interne : 000D84 ( Main/Merge ); précédent : 000D83; suivant : 000D85

A Difference of Convex Functions Algorithm for Switched Linear Regression

Auteurs : Tao Pham Dinh [France] ; Hoai Minh Le [France] ; Hoai An Le Thi [France] ; Fabien Lauer [France]

Source :

IEEE Transactions on Automatic Control [ 0018-9286 ] ; 2014.

RBID : Hal:hal-00931206

English descriptors

mix :
- DC programming, DCA, Nonconvex optimization, Nonsmooth optimization, Piecewise affine systems, Switched linear systems, Switched regression, System identification.

Abstract

This paper deals with switched linear system identification and more particularly aims at solving switched linear regression problems in a large-scale setting with both numerous data and many parameters to learn. We consider the recent minimum-of-error framework with a quadratic loss function, in which an objective function based on a sum of minimum errors with respect to multiple submodels is to be minimized. The paper proposes a new approach to the optimization of this nonsmooth and nonconvex objective function, which relies on Difference of Convex (DC) functions programming. In particular, we formulate a proper DC decomposition of the objective function, which allows us to derive a computationally efficient DC algorithm. Numerical experiments show that the method can efficiently and accurately learn switching models in large dimensions and from many data points.

Url:

https://hal.archives-ouvertes.fr/hal-00931206

DOI: 10.1109/TAC.2014.2301575

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to stream Hal, to step Curation: 000162
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Links to Exploration step

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Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper deals with switched linear system identification and more particularly aims at solving switched linear regression problems in a large-scale setting with both numerous data and many parameters to learn. We consider the recent minimum-of-error framework with a quadratic loss function, in which an objective function based on a sum of minimum errors with respect to multiple submodels is to be minimized. The paper proposes a new approach to the optimization of this nonsmooth and nonconvex objective function, which relies on Difference of Convex (DC) functions programming. In particular, we formulate a proper DC decomposition of the objective function, which allows us to derive a computationally efficient DC algorithm. Numerical experiments show that the method can efficiently and accurately learn switching models in large dimensions and from many data points.</div>
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	Serveur d'exploration sur la recherche en informatique en Lorraine
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Serveur d'exploration sur la recherche en informatique en Lorraine

A Difference of Convex Functions Algorithm for Switched Linear Regression

A Difference of Convex Functions Algorithm for Switched Linear Regression

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