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

Proceedings of the American Control Conference

Volumn , Issue , 2013, Pages 327-333

  Giselsson, Pontus ^a  

^a LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

GRADIENT METHODS;

COMPLEXITY BOUNDS; CONVERGENCE RATES; DISTRIBUTED METHODS; DISTRIBUTED MODEL PREDICTIVE CONTROL; DISTRIBUTED OPTIMIZATION; GRADIENT BASED; GRADIENT BASED OPTIMIZATION ALGORITHMS; NUMBER OF ITERATIONS;

MODEL PREDICTIVE CONTROL;

EID: 84883505849     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/acc.2013.6579858     Document Type: Conference Paper

References (24)

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. D. P. Bertsekas. Nonlinear programming. Athena Scientific, Belmont, MA, 2nd edition, 1999.
3. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1-122, 2011.
4. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004.
5. O. Devolder, F. Glineur, and Y. Nesterov. First-order methods of smooth convex optimization with inexact oracle. Submitted to Mathematical Programming, available http://www. optimization-online. org/DB-FILE/ 2010/12/2865. pdf, 2010.
6. M. D. Doan, T. Keviczky, I. Necoara, M. Diehl, and B. De Schutter. A distributed version of Han's method for DMPC using local communications only. Control Engineering and Applied Informatics, 11(3):6-15, 2009.
7. P. Giselsson. Gradient-Based Distributed Model Predictive Control. PhD thesis, Department of Automatic Control, Lund University, Sweden, November 2012.
8. P. Giselsson. Optimal preconditioning and iteration complexity bounds for gradient-based optimization in model predictive control. In 2013 American Control Conference, Washington, D. C., 2013. Accepted for publication.
9. 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.
10. P. Giselsson and A. Rantzer. On feasibility, stability and performance in distributed model predictive control. IEEE Transactions on Automatic Control, 2013. Accepted for publication.
11. C. N. Jones and M. Morari. Approximate Explicit MPC using Bilevel Optimization. In European Control Conference, Budapest, Hungary, August 2009.
12. J. Löfberg. Yalmip : A toolbox for modeling and optimization in MATLAB. In Proceedings of the CACSD Conference, Taipei, Taiwan, 2004.
13. R. R. Negenborn, B. De Schutter, and J. Hellendoorn. Multi-agent model predictive control for transportation networks: Serial versus parallel schemes. Engineering Applications of Artificial Intelligence, 21(3):353-366, April 2008.
14. A. Nemirovsky and D. Yudin. Informational complexity and efficient methods for solution of convex extremal problems. Wiley, New York, 1983.
15. Y. Nesterov. A method of solving a convex programming problem with convergence rate o (1/k2). Soviet Mathematics Doklady, 27(2):372-376, 1983.
16. Y. Nesterov. On an approach to the construction of optimal methods of minimization of smooth convex functions. Ekonom. i. Mat. Metody, 24:509-517, 1988.
17. Y. Nesterov. Introductory Lectures on Convex Optimization: A Basic Course (Applied Optimization). Springer Netherlands, 1 edition, 2003.
18. Y. Nesterov. Smooth minimization of non-smooth functions. Math. Program., 103(1):127-152, May 2005.
19. J. F. Sturm. Using SeDuMi 1. 02, a MATLAB toolbox for optimization over symmetric cones. Optimization Methods and Software, 11-12:625-653, 1999.
20. P. Trnka, J. Pekar, and V. Havlena. Application of distributed MPC to Barcelona water distribution network. In Proceedings of the 18th IFAC World Congress, Milan, Italy, 2011.
21. P. Tseng. On accelerated proximal gradient methods for convexconcave optimization. Technical report, Department of Mathematics, University of Washington, 2008.
22. Y. Wakasa, M. Arakawa, K. Tanaka, and T. Akashi. Decentralized model predictive control via dual decomposition. In Proceedings of the 47th IEEE Conference on Decision and Control, pages 381-386, Cancun, Mexico, December 2008.
23. X-Y. Zhao, D. Sun, and K-C. Toh. A Newton-cg augmented Lagrangian method for semidefinite programming. SIAM J. on Optimization, 20(4):1737-1765, January 2010.
24. 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.