A proximal center-based decomposition method for multi-agent convex optimization

Proceedings of the IEEE Conference on Decision and Control

Volumn , Issue , 2008, Pages 3077-3082

  Necoara, Ion ^a   Suykens, Johan A K ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION; LAGRANGE MULTIPLIERS; MODEL PREDICTIVE CONTROL;

CONVEX OBJECTIVES; COUPLED CONSTRAINTS; DECOMPOSITION METHODS; DISTRIBUTED MODEL PREDICTIVE CONTROL; DUAL DECOMPOSITION; NUMBER OF ITERATIONS; OBJECTIVE FUNCTIONS; SMOOTHING TECHNIQUES;

MULTI AGENT SYSTEMS;

EID: 62949197373     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2008.4738764     Document Type: Conference Paper

References (14)

1. D. P. Bertsekas and J. N. Tsitsiklis. Parallel and distributed computation: Numerical Methods. Prentice-Hall, Englewood Cliffs, NJ, 1989.
2. G. Chen and M. Teboulle. A proximal-based decomposition method for convex minimization problems. Mathematical Programming (A), 64:81-101, 1994.
3. G. Cohen. Optimization by decomposition and coordination: A unified approach. IEEE Transactions on Automatic Control, AC-23(2):222-232, 1978.
4. J. Eckstein and D. Bertsekas. On the Douglas-Rachford splitting method and the proximal point method for maximal monotone operators. Mathematical Programming (A), 55(3):293-318, 1992.
5. S. Kontogiorgis, R. De Leone, and R. Meyer. Alternating direction splitings for block angular parallel optimization. Journal of Optimization Theory and Applications, 90(1):1-29, 1996.
6. I. Necoara and J.A.K. Suykens. Application of a smoothing technique to decomposition in convex programming. Technical Report 08-07, ESAT-SISTA, K.U. Leuven (Leuven, Belgium), January 2008.
7. Y. Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Kluwer, Boston, 2004.
8. Y. Nesterov. Smooth minimization of non-smooth functions. Mathematical Programming (A), 103(1):127-152, 2005.
9. D. Palomar and M. Chiang. Alternative distributed algorithms for network utility maximization: Framework and applications. IEEE Transactions on Automatic Control, 52(12):2254-2268, 2007.
10. R. T. Rockafellar. Convex Analysis, volume 28 of Princeton Mathematics Series. Princeton University Press, 1970.
11. J. E. Spingarn. Applications of the method of partial inverses to convex programming: decomposition. Mathematical Programming (A), 32:199-223, 1985.
12. P. Tseng. Applications of a splitting algorithm to decomposition in convex programming and variational inequalities. SIAM Journal of Control and Optimization, 29(1):119-138, 1991.
13. A. Venkat, I. Hiskens, J. Rawlings, and S. Wright. Distributed MPC strategies with application to power system automatic generation control. IEEE Transactions on Control Systems Technology, to appear, 2007.
14. L. Xiao, M. Johansson, and S. Boyd. Simultaneous routing and resource allocation via dual decomposition. IEEE Transactions on Communications, 52(7):1136-1144, 2004.