Local Convergence of Sequential Convex Programming for Nonconvex Optimization
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.
KeywordsOptimal Control Problem Sequential Quadratic Programming Local Convergence Control Torque Strong Regularity
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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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