Classical and relaxed progressively refining discretization-optimization methods for optimal control problems defined by ordinary differential equations

Chryssoverghi, I; Coletsos, J; Kokkinis, B

dc.contributor.author	Chryssoverghi, I	en
dc.contributor.author	Coletsos, J	en
dc.contributor.author	Kokkinis, B	en
dc.date.accessioned	2014-03-01T02:53:34Z
dc.date.available	2014-03-01T02:53:34Z
dc.date.issued	2012	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36421
dc.subject.other	Classical control	en
dc.subject.other	Classical solutions	en
dc.subject.other	Computational costs	en
dc.subject.other	Descent method	en
dc.subject.other	Discretizations	en
dc.subject.other	Gradient projection methods	en
dc.subject.other	Nonlinear ordinary differential equation	en
dc.subject.other	Numerical example	en
dc.subject.other	Optimal control problem	en
dc.subject.other	Optimality	en
dc.subject.other	Pointwise state constraints	en
dc.subject.other	Relaxed control	en
dc.subject.other	Computational efficiency	en
dc.subject.other	Optimal control systems	en
dc.subject.other	Refining	en
dc.subject.other	Ordinary differential equations	en
dc.title	Classical and relaxed progressively refining discretization-optimization methods for optimal control problems defined by ordinary differential equations	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-29843-1_11	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-29843-1_11	en
heal.publicationDate	2012	en
heal.abstract	An optimal control problem is considered, for systems defined by nonlinear ordinary differential equations, with control and pointwise state constraints. Since the problem may have no classical solutions, it is also formulated in the relaxed form. Various necessary/sufficient conditions for optimality are first given for both formulations. In order to solve these problems numerically, we then propose a discrete penalized gradient projection method generating classical controls, and a discrete penalised conditional descent method generating relaxed controls. In both methods, the discretization procedure is progressively refining in order to achieve efficiency with reduced computational cost. Results are given concerning the behaviour in the limit of these methods. Finally, numerical examples are provided. © 2012 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-29843-1_11	en
dc.identifier.volume	7116 LNCS	en
dc.identifier.spage	106	en
dc.identifier.epage	114	en