Discrete methods for optimal control problems

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dc.contributor.author Chryssoverghi, I en
dc.date.accessioned 2014-03-01T01:18:53Z
dc.date.available 2014-03-01T01:18:53Z
dc.date.issued 2003 en
dc.identifier.issn 0302-9743 en
dc.identifier.uri http://hdl.handle.net/123456789/15244
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-35248826475&partnerID=40&md5=329323670ad6e08acfa156e2898e0863 en
dc.subject.classification Computer Science, Theory & Methods en
dc.subject.other APPROXIMATION en
dc.title Discrete methods for optimal control problems en
heal.type journalArticle en
heal.language English en
heal.publicationDate 2003 en
heal.abstract We consider a constrained optimal control problem, which we formulate in classical and in relaxed form. In order to approximate this problem numerically, we apply various discretization schemes on either of these two forms and study the behavior in the limit of discrete optimality and necessary conditions for optimality. We then propose discrete mixed gradient penalty methods that use classical or relaxed discrete controls and progressively refine the discretization, thus reducing computing time and memory. In addition, when the discrete adjoint state is not defined or difficult to calculate, we propose discrete methods that use approximate adjoints and derivatives. The result is that in relaxed methods accumulation points of generated sequences satisfy continuous strong relaxed optimality conditions, while in classical methods they satisfy weak optimality conditions. © Springer-Verlag Berlin Heidelberg 2003. en
heal.publisher SPRINGER-VERLAG BERLIN en
heal.journalName Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.identifier.isi ISI:000182433500022 en
dc.identifier.volume 2542 en
dc.identifier.spage 205 en
dc.identifier.epage 212 en

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