A method of multipliers algorithm for sparsity-promoting optimal control

Neil K. Dhingra, Mihailo R. Jovanović

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

We develop a customized method of multipliers algorithm to efficiently solve a class of regularized optimal control problems. By exploiting the problem structure, we transform the augmented Lagrangian into a form which can be efficiently minimized using proximal methods. We apply our algorithm to an ℓ1-regularized state-feedback optimal control problem and compare its performance with a proximal gradient algorithm and an alternating direction method of multipliers algorithm. In contrast to other methods, our algorithm has both a theoretical guarantee of convergence and fast computation speed in practice.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1942-1947
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

Keywords

  • Augmented Lagrangian
  • Method of multipliers
  • Non-smooth optimization
  • Proximal methods
  • Sparsity-promoting optimal control
  • Structure identification

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