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

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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 http://hdl.handle.net/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


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