Local Convergence of Sequential Convex Programming for Nonconvex Optimization

  • Quoc Tran Dinh
  • Moritz Diehl
Conference paper


This paper introduces sequential convex programming (SCP), a local optimzation method for solving nonconvex optimization problems. A full-step SCP algorithm is presented. Under mild conditions the local convergence of the algorithm is proved as a main result of this paper. An application to optimal control illustrates the performance of the proposed algorithm.


Optimal Control Problem Sequential Quadratic Programming Local Convergence Control Torque Strong Regularity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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This research was supported by Research Council KUL: CoE EF/05/006 Optimization in Engineering(OPTEC), GOA AMBioRICS, IOF-SCORES4CHEM, several PhD/postdoc & fellow grants; the Flemish Government via FWO: PhD/postdoc grants, projects G.0452.04, G.0499.04, G.0211.05, G.0226.06, G.0321.06, G.0302.07, G.0320.08 (convex MPC), G.0558.08 (Robust MHE), G.0557.08, G.0588.09, research communities (ICCoS, ANMMM, MLDM) and via IWT: PhD Grants, McKnow-E, Eureka-Flite+EU: ERNSI; FP7-HD-MPC (Collaborative Project STREP-grantnr. 223854), Erbocon, Contract Research: AMINAL, and Helmholtz Gemeinschaft: viCERP; Austria: ACCM, and the Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-2011).

The authors are very much thankful to the anonymous referees, who corrected numerous mistakes and suggested several improvements.


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© Springer -Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Electrical Engineering (SCD-ESAT) and OPTECKatholieke Universiteit LeuvenHeverleeBelgium

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