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Thesis

Dimensionality reduction techniques for global optimization

Abstract:

Though ubiquitous in applications, global optimisation problems are generally the most computationally intense due to their solution time growing exponentially with linear increase in their dimensions (this is the well known/so called ‘curse of dimensionality’). In this thesis, we show that this scalability — and sometimes even tractability — challenges can be overcome in the global optimization of functions with low effective dimensionality, that are constant along an (unknown) linear sub...

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Division:
MPLS
Department:
Mathematical Institute
Role:
Author

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Role:
Supervisor
ORCID:
0000-0002-0963-5550
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Programme:
The Alan Turing Institute doctoral studentship
Grant:
EP/N510129/1
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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