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:
https://lup.lub.lu.se/record/3692041
- author
- Giselsson, Pontus
LU
- organization
- publishing date
- 2013
- 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
- 2023-09-03 02:48:38
@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}},
}