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Accelerated gradient methods and dual decomposition in distributed model predictive control

Giselsson, Pontus LU orcid ; Doan, Dang ; Keviczky, Tamas ; De Schutter, Bart and Rantzer, Anders LU orcid (2013) In Automatica 49(3). p.829-833
Abstract
We propose a distributed optimization algorithm for mixed

L_1/L_2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1/k^2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1/k). The performance of the developed algorithm is evaluated on randomly generated optimization problems arising in distributed model predictive control (DMPC). The evaluation shows that, when the problem data is sparse and large-scale, our algorithm can outperform current state-of-the-art optimization software CPLEX and MOSEK.
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Automatica
volume
49
issue
3
pages
829 - 833
publisher
Pergamon Press Ltd.
external identifiers
  • wos:000316590300017
  • scopus:84875216516
ISSN
0005-1098
DOI
10.1016/j.automatica.2013.01.009
project
LCCC
language
English
LU publication?
yes
additional info
key= gis2012aut
id
ee512043-aeca-4559-bf78-5cf1cbc81547 (old id 2278086)
date added to LUP
2016-04-01 14:31:58
date last changed
2024-05-09 01:18:16
@article{ee512043-aeca-4559-bf78-5cf1cbc81547,
  abstract     = {{We propose a distributed optimization algorithm for mixed<br/><br>
L_1/L_2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1/k^2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1/k). The performance of the developed algorithm is evaluated on randomly generated optimization problems arising in distributed model predictive control (DMPC). The evaluation shows that, when the problem data is sparse and large-scale, our algorithm can outperform current state-of-the-art optimization software CPLEX and MOSEK.}},
  author       = {{Giselsson, Pontus and Doan, Dang and Keviczky, Tamas and De Schutter, Bart and Rantzer, Anders}},
  issn         = {{0005-1098}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{829--833}},
  publisher    = {{Pergamon Press Ltd.}},
  series       = {{Automatica}},
  title        = {{Accelerated gradient methods and dual decomposition in distributed model predictive control}},
  url          = {{https://lup.lub.lu.se/search/files/4027400/3131655.pdf}},
  doi          = {{10.1016/j.automatica.2013.01.009}},
  volume       = {{49}},
  year         = {{2013}},
}