Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 16 of 16 for “"distributionally-robust optimization"”.
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Distributionally robust optimization with marginals : theory and applications
In this thesis, we consider distributionally robust optimization (DRO) problems in which the ambiguity sets are designed from marginal distribution information - more specifically, when the ambiguity set includes any distribution whose marginals are consistent with given prescribed distributions …
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Distributionally robust optimization for design under partially observable uncertainty
… thesis addresses this challenge by formulating a distributionally robust design optimization problem, and presents computationally efficient algorithms for solving the problem. In distributionally robust optimization (DRO) methods, the designer acknowledges that they are unable to exactly specify …
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RISK- AND AMBIGUITY-AVERSE OPTIMIZATION MADE MORE TRACTABLE AND LESS CONSERVATIVE
This thesis builds on the recent developments in distributionally robust optimization and satisficing and proposes how robust decisions under uncertainty can be efficiently made when the decision maker exhibits risk aversion, ambiguity aversion and regret aversion. Theoretically, we develop a new …
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Distributionally robust solution schemes for two-stage optimization and interdiction problems under uncertainty
… in the presence of uncertainty. One can use optimization models with uncertain parameters to formulate the decision problems. Despite its wide applications in real-world problems, optimization under uncertainty gives rise to computational challenges. This thesis aims to design tractable …
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Data-driven robust solution schemes for sequential decision making
This dissertation develops robust and data-efficient methodologies for sequential decision making under uncertainty, motivated by challenges arising in operations research, control, and machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk …
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Gradient Subgroup Scanning for Distributionally and Outlier Robust Models
… training data point (as is the case in group distributionally robust optimization (DRO)), or every validation data point. Furthermore, these distributionally robust approaches tend to show reduced performance when outliers are also present in the data. Unfortunately, existing methods for …
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Robust data-driven optimization for dynamic and decision-dependent systems under uncertainty
… dissertation develops a unified framework for robust data-driven optimization in dynamic and decision-dependent systems. Across three research directions, this work introduces new algorithmic methods and theoretical results that enable decision-makers to make better decisions. The first part of …
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Efficient Robustness and Interpretability in Learning and Data-Driven Decision-Making
… learning, emphasizing two critical dimensions: Robustness and Interpretability. The first part of this thesis focuses on robustness, which guarantees that algorithms deliver stable and predictable performance despite various data uncertainties. We study robustness when learning under diverse …
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Decomposition methods for large scale stochastic and robust optimization problems
… for use on broad families of stochastic and robust optimization problems in order to yield tractable approaches for large-scale real world application. We introduce a new type of a Markov decision problem named the Generalized Rest less Bandits Problem that encompasses a broad generalization …
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Algorithms for Distributionally Risk-Receptive and Robust Stochastic Integer Programs and Interdiction Problems
… problems. We introduce models within both distributionally risk-receptive (DRR) and distributionally robust optimization (DRO) frameworks, allowing for an adjustment of decision-maker's risk attitudes in adversarial settings. A key contribution is the study of an optimistic perspective, …
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From data to decisions through new interfaces between optimization and statistics
… enabled by novel connections we uncover between optimization and statistics. We pursue fundamental theory, specific methodologies, and revealing applications that advance data analytics from a tool of understanding to a decision-making engine. In part I, we focus on the interface between …
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Energy Management of Electric Vehicle Supply Equipment in Multi-Unit Residential Buildings
… EV availability across multiple sub-feeders, a Distributionally Robust Optimization (DRO) framework with a Wasserstein ambiguity set is developed to determine the optimal charging profiles for the EVSEs under such uncertain conditions. Furthermore, to better capture the hierarchical and …
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Distributionally Ambiguous Stackelberg Combinatorial Games for Submodular Optimization and Camera View-Frame Placement
… defender's recourse is a complex com- binatorial optimization problem and the attacker faces uncertainty and distributional am- biguity. We analyze these games through two complementary frameworks. Distributionally Robust Optimization (DRO) framework provides a risk-averse attacker with robust …
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Model-Based and Data-Driven Covariance Control: Theory and Applications
… methods often struggle with ensuring safety and robustness in stochastic environments. This dissertation advances the theory of covariance steering (CS), which shifts the focus from controlling specific system states to steering entire state distributions under constraints. Historically, CS has …
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Data-driven decision-making under uncertainty in power systems
… nine publications, each of which lays out an optimization framework under uncertainty or a decision-support tool, on which SOs can capitalize in ensuring a reliable power system operation around the clock. The deepening penetration of renewables greatly exacerbates the uncertainty and …