Improved dual decomposition for distributed model predictive control

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 19, Issue , 2014, Pages 1203-1209

  Giselsson, Pontus ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; AUTOMATION; GRADIENT METHODS; OPTIMIZATION;

CENTRALIZED COMPUTATION; CONVERGENCE PROPERTIES; DISTRIBUTED IMPLEMENTATION; DISTRIBUTED MODEL PREDICTIVE CONTROL; DISTRIBUTED RECONFIGURATION; FAST GRADIENT METHODS; OPTIMIZATION PROBLEMS; QUADRATIC APPROXIMATION;

MODEL PREDICTIVE CONTROL;

EID: 84929762249     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20140824-6-za-1003.00294     Document Type: Conference Paper

References (19)

1. A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sciences, 2(1):183-202, October 2009.
2. A. Beck, A. Nedic, A. Ozdaglar, and M. Teboulle. Optimal distributed gradient methods for network resource allocation problems. IEEE Transactions on Control of Network Systems, 2014. To appear.
3. J. F. Benders. Partitioning procedures for solving mixedvariables programming problems. Numerische Mathematik, 4(1):238-252, 1962.
4. G. B. Danzig and P. Wolfe. The decomposition algorithm for linear programming. Econometrica, 4:767-778, 1961.
5. O. Devolder, F. Glineur, and Y. Nesterov. First-order methods of smooth convex optimization with inexact oracle. Mathematical Programming, pages 1-39, 2013.
6. M. D. Doan, T. Keviczky, and B. De Schutter. An iterative scheme for distributed model predictive control using Fenchel's duality. Journal of Process Control, 21(5): 746-755, June 2011. Special Issue on Hierarchical and Distributed Model Predictive Control.
7. H. Everett. Generalized Lagrange multiplier method for solving problems of optimum allocation of resources. Operations Research, 11:399-417, 1963.
8. P. Giselsson. A generalized distributed accelerated gradient method for DMPC with iteration complexity bounds. In Proceedings of 2013 American Control Conference, pages 327-333, Washington D.C., June 2013.
9. P. Giselsson. Improving fast dual ascent for MPC - Part I: The distributed case. Automatica, 2014. Submitted. Available http://arxiv.org/abs/1312.3012.
10. P. Giselsson, M. D. Doan, T. Keviczky, B. De Schutter, and A. Rantzer. Accelerated gradient methods and dual decomposition in distributed model predictive control. Automatica, 49(3):829-833, 2013.
11. M. Kraning, E. Chu, J. Lavaei, and S. Boyd. Dynamic network energy management via proximal message passing. Foundations and Trends in Optimization, 1(2):70-122, 2013.
12. R. R. Negenborn. Multi-Agent Model Predictive Control with Applications to Power Networks. PhD thesis, TU Delft, 2007.
13. 2). Soviet Mathematics Doklady, 27(2):372-376, 1983.
14. Y. Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Springer Netherlands, 1st edition, 2003. ISBN 1402075537.
15. Y. Nesterov. Smooth minimization of non-smooth functions. Math. Program., 103(1):127-152, May 2005.
16. P. Patrinos and A. Bemporad. An accelerated dual gradient-projection algorithm for embedded linear model predictive control. IEEE Transactions on Automatic Control, 59(1):18-33, 2014.
17. S. Richter, C. N. Jones, and M. Morari. Certification aspects of the fast gradient method for solving the dual of parametric convex programs. Mathematical Methods of Operations Research, 77(3):305-321, 2013.
18. P. Tseng. On accelerated proximal gradient methods for convex-concave optimization. Technical report. Available: http://www.csie.ntu.edu.tw/~b97058/tseng/papers/apgm.pdf, May 2008.
19. W. Zuo and Z. Lin. A generalized accelerated proximal gradient approach for total-variation-based image restoration. IEEE Transactions on Image Processing, 20(10):2748-2759, October 2011.