Online Algorithms for Dynamic Aggregation Problems

Kavouras, Loukas

dc.contributor.author	Kavouras, Loukas
dc.date.accessioned	2021-12-22T09:11:35Z
dc.date.available	2021-12-22T09:11:35Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/54233
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.21931
dc.rights	Default License
dc.subject	αλγοριθμοι	el
dc.subject	αμεσοι αλγοριθμοι	el
dc.subject	βελτιστοποιηση	el
dc.subject	μαθηση	el
dc.subject	δυναμικη συναθροιση	el
dc.subject	algorithms	en
dc.subject	online algorithms	el
dc.subject	optimization	el
dc.subject	learning	el
dc.subject	dynamic aggregation	el
dc.title	Online Algorithms for Dynamic Aggregation Problems	en
heal.type	doctoralThesis
heal.classification	Computer Science	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2021-05-27
heal.abstract	In this Ph.D thesis, we study online variants of Dynamic Aggregation problems that are generalizations of prominent and well studied online problems. In the online setting, we additionally assume that the input arrives piece-by-piece and that the online algorithm has to provide a solution for the input piece of the current stage before it sees the upcoming input pieces of future stages. The decision quality of the online algorithm is evaluated against an optimal offline algorithm, which is given the whole problem data from the beginning. The performance of the online algorithm is measured by the competitive ratio which is the worst-case ratio between the online cost and the optimal offline cost. We consider the online variants of the Min-Sum Set Cover problem, the K-Facility Reallocation problem and the Dynamic Facility Location problem. For all the aforementioned problems, we design online algorithms and we prove upper bounds on their competitive ratio. Moreover, we construct difficult instances for these problems and we prove lower bounds on the competitive ratio of online algorithms on these instances. The majority the upper bounds are close (or the same) with the lower bounds that we prove and this ensures that our online algorithms are optimal or near optimal.	en
heal.advisorName	Fotakis, Dimitris
heal.committeeMemberName	Fotakis, Dimitris
heal.committeeMemberName	Pagourtzis, Aris
heal.committeeMemberName	Emiris, Ioannis
heal.committeeMemberName	Symvonis, Antonios
heal.committeeMemberName	Kontogiannis, Spyros
heal.committeeMemberName	Zisimopoulos, Vasilis
heal.committeeMemberName	Stamou, Giorgos
heal.academicPublisher	Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.fullTextAvailability	false