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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 1Affine decision rule
- 1Chance-constrained optimization
- 1Distributionally robust optimization
- 1Fault detection
- 1Gaussian Mixture Models
- 1Optimal design and operation
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Fall 2024
years. The optimal transport problem seeks to transport probability mass from one probability distribution to another at the least total cost. This thesis uses this underlying concept in three main ways. Firstly, the optimal transport distance is used as a measure of similarity between probability
ambiguity on multimodal uncertainty that is modeled as a Gaussian mixture. An optimal transport variant for Gaussian mixtures is further used to construct an ambiguity set of distributions around this reference model, and a tractable formulation is presented. The superior performance of this proposed
formulation is contrasted with the established Wasserstein method on an illustrative study, as well as on a portfolio optimization problem. The thesis then uses the proposed formulation to tackle chance-constrained optimization in a distributionally robust setting, wherein the worst-case expected constraint
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Fall 2019
uncertainty realized and presents a conservative solution to the problem that would be valid for any realization of uncertainty it was solved for. In contrast, stochastic optimization deals with uncertainty in an optimization problem by assuming that the probability distribution of the uncertainty is known
presents an additional layer of complexity owing to the presence of uncertainty in the operation of the system. This uncertainty may come from a variety of sources, such as effluent flow rate, contaminant concentration, and treatment unit removal efficiency. Therefore, the need to focus on developing a
The optimal design and operation of effluent treatment system networks poses a significant cant challenge in the present time, with the imposition of stricter environmental regulations and an increased demand for resources exacerbated by a diminishing resource pool. In practice, this problem