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Metric selection in fast dual forward-backward splitting

Författare

Summary, in English

The performance of fast forward-backward splitting, or equivalently fast proximal gradient methods, depends on the conditioning of the optimization problem data. This conditioning is related to a metric that is defined by the space on which the optimization problem is stated; selecting a space on which the optimization data is better conditioned improves the performance of the algorithm. In this paper, we propose several methods, with different computational complexity, to find a space on which the algorithm performs well. We evaluate the proposed metric selection procedures by comparing the performance to the case when the Euclidean space is used. For the most ill-conditioned problem we consider, the computational complexity is improved by two to three orders of magnitude. We also report comparable to superior performance compared to state-of-the-art optimization software. (C) 2015 Elsevier Ltd. All rights reserved.

Publiceringsår

2015

Språk

Engelska

Sidor

1-10

Publikation/Tidskrift/Serie

Automatica

Volym

62

Dokumenttyp

Artikel i tidskrift

Förlag

Pergamon Press Ltd.

Ämne

  • Control Engineering

Nyckelord

  • First order optimization algorithms
  • Metric selection
  • Preconditioning
  • Model predictive control
  • Distributed optimization

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0005-1098