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A generalized distributed accelerated gradient method for distributed model predictive control with iteration complexity bounds

Giselsson, Pontus LU (2013) American Control Conference, 2013 p.327-333
Abstract
Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number of iterations needed to achieve satisfactory accuracy might be significant. This is not a desirable characteristic for distributed optimization in distributed model predictive control. Rather, the number of iterations should be kept low to reduce communication requirements, while the complexity within an iteration can be significant. By incorporating Hessian information in a distributed accelerated gradient method in a well-defined manner, we are able to significantly reduce the number of iterations needed to achieve... (More)
Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number of iterations needed to achieve satisfactory accuracy might be significant. This is not a desirable characteristic for distributed optimization in distributed model predictive control. Rather, the number of iterations should be kept low to reduce communication requirements, while the complexity within an iteration can be significant. By incorporating Hessian information in a distributed accelerated gradient method in a well-defined manner, we are able to significantly reduce the number of iterations needed to achieve satisfactory accuracy in the solutions, compared to distributed methods that are strictly gradient-based. Further, we provide convergence rate results and iteration complexity bounds for the developed algorithm. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
[Host publication title missing]
pages
327 - 333
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
American Control Conference, 2013
conference location
Washington, DC, United States
conference dates
2013-06-17 - 2016-06-19
external identifiers
  • wos:000327210200055
  • scopus:84883505849
ISSN
0743-1619
project
LCCC
language
English
LU publication?
yes
id
61d2b655-cd99-4dc6-9303-50246c9410d0 (old id 3692041)
date added to LUP
2016-04-01 13:38:20
date last changed
2020-04-01 03:54:52
@inproceedings{61d2b655-cd99-4dc6-9303-50246c9410d0,
  abstract     = {Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number of iterations needed to achieve satisfactory accuracy might be significant. This is not a desirable characteristic for distributed optimization in distributed model predictive control. Rather, the number of iterations should be kept low to reduce communication requirements, while the complexity within an iteration can be significant. By incorporating Hessian information in a distributed accelerated gradient method in a well-defined manner, we are able to significantly reduce the number of iterations needed to achieve satisfactory accuracy in the solutions, compared to distributed methods that are strictly gradient-based. Further, we provide convergence rate results and iteration complexity bounds for the developed algorithm.},
  author       = {Giselsson, Pontus},
  booktitle    = {[Host publication title missing]},
  issn         = {0743-1619},
  language     = {eng},
  pages        = {327--333},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {A generalized distributed accelerated gradient method for distributed model predictive control with iteration complexity bounds},
  year         = {2013},
}