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dc.contributor.authorGiang, Tran Khanh Hung
dc.date.accessioned2024-01-25T00:52:25Z
dc.date.available2024-01-25T00:52:25Z
dc.date.issued2024-01-25
dc.identifier.urihttps://hdl.handle.net/2123/32130
dc.description.abstractWhen faced with multiple minima of an "inner-level" convex optimisation problem, the convex bilevel optimisation problem selects an optimal solution which also minimises an auxiliary "outer-level" convex objective of interest. Bilevel optimisation requires a different approach compared to single-level optimisation problems since the set of minimisers for the inner-level objective is not given explicitly. In this thesis, we propose new projection-free methods for convex bilevel optimisation which require only a linear optimisation oracle over the base domain. We provide convergence guarantees for both inner- and outer-level objectives that hold under our proposed projection-free methods. In particular, we highlight how our guarantees are affected by the presence or absence of an optimal dual solution. Lastly, we conduct numerical experiments that demonstrate the performance of the proposed methods.en_AU
dc.language.isoenen_AU
dc.subjectbilevel optimizationen_AU
dc.subjectprojection-freeen_AU
dc.subjectconditional gradienten_AU
dc.titleProjection-free methods for solving smooth convex bilevel optimisation problemsen_AU
dc.typeThesisen_AU
dc.type.thesisHonoursen_AU
usyd.facultySeS faculties schools::The University of Sydney Business Schoolen_AU
usyd.departmentDiscipline of business analyticsen_AU
workflow.metadata.onlyNoen_AU


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