Accelerated gradient methods and dual decomposition in distributed model predictive control
Author
Summary, in English
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.
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.
Department/s
Publishing year
2013
Language
English
Pages
829-833
Publication/Series
Automatica
Volume
49
Issue
3
Full text
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Links
Document type
Journal article
Publisher
Pergamon Press Ltd.
Topic
- Control Engineering
Status
Published
Project
- LCCC
Research group
- LCCC
ISBN/ISSN/Other
- ISSN: 0005-1098