Accelerated gradient methods and dual decomposition in distributed model predictive control
Författare
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.
Avdelning/ar
Publiceringsår
2013
Språk
Engelska
Sidor
829-833
Publikation/Tidskrift/Serie
Automatica
Volym
49
Issue
3
Fulltext
- Available as PDF - 167 kB
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Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Pergamon Press Ltd.
Ämne
- Control Engineering
Status
Published
Projekt
- LCCC
Forskningsgrupp
- LCCC
ISBN/ISSN/Övrigt
- ISSN: 0005-1098