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 “"bilevel optimization"”.
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Bayesian bilevel optimization
In this dissertation, we focus on improving bilevel optimization through several approaches developed during research. Bilevel optimization problems consist of upper-level and lower-level optimization problems connected hierarchically. Upper-level and lower-level problems are also referred to as …
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BILEVEL OPTIMIZATION FOR FAST CONTROL MOMENT GYRO MANEUVERS
… torque allocation as a constrained static optimization problem; previous efforts solved this problem by developing a set of algebraic differential equations that converge upon a solution. This thesis proposes a bilevel optimization problem, in which a higher-level minimum-time spacecraft …
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On Bilevel Optimization without Full Unrolls: Methods and Applications
Bilevel optimization (BLO) problems are nested optimization problems where an outer objective must be minimized subject to the optimality of an inner objective. This nested structure poses several challenges, including the cost of running full unrolls of the inner problem for each outer parameter …
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Bilevel Optimization in the Deep Learning Era: Methods and Applications
Neural networks, coupled with their associated optimization algorithms, have demonstrated remarkable efficacy and versatility across an extensive array of tasks, encompassing image recognition, speech recognition, object detection, sentiment analysis, and more. The inherent strength of neural …
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Novel first-order methods for bilevel and minimax optimization.
Bilevel and minimax optimization problems arise in various fields, including machine learning, game theory, and decision science. This thesis highlights the underlying connections between constrained minimax and bilevel optimization and develops novel first-order methods with strong theoretical …
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Beyond Gradients: Using Curvature Information for Deep Learning
This thesis investigates optimization and interpretability techniques for deep learning, extending beyond gradient-based methods to incorporate curvature information. We begin by addressing the limitations of first-order optimization methods like gradient descent, which can exhibit slow convergence …
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Dvojúrovňové optimalizačné modely a ich využitie v úlohách optimalizácie portfólia
Title: Bilevel optimization problems and their applications to portfolio selection Author: Lenka Godul'ová Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Ing. Miloš Kopa, Ph.D. Abstract: This work deals with the problem of bilevel tasks. First, it recalls the basic …
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Multiclass Origin-Destination Estimation Using Multiple Data Types
… damage, etc of trucks. This thesis proposes a bilevel optimization model and corresponding solution method for static multi-class O-D estimation using various types of data. Limited memory BFGS method with bounded constraints is used for solving the upper level optimization, which is used to …
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A hierarchical decision-making approach to resource management and valuation: The case of conjunctive water use
… for the latter specification. An alternative bilevel optimization framework is proposed to address the issue. In this approach, the hierarchical decision making, water prices and the profit maximizing behavior of the farmers are incorporated explicitly, for alternative global objective …
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Methods Of Distributed And Nonlinear Process Control: Structuredness, Optimality And Intelligence
… algorithm for nonconvex constrained distributed optimization with theoretically provable convergence properties -- ELLADA, which is applied to distributed nonlinear model predictive control of a benchmark process system. We derive bilevel optimization formulations for the Lyapunov stability …
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Towards Effective Tools for Debugging Machine Learning Models
… model’s parameters. We formulate the update as a bilevel optimization problem that requires the updated model to match the expert’s predictions and feature annotations on the audit set. Model guiding can be used to identify and correct mislabelled examples. Similarly, we show that the approach can …
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Shape Calculus Applied to State-Constrained Elliptic Optimal Control Problems
… active set appears as an independent and equal optimization variable in this new formulation. Thereby a new class of optimization problem is established, which forms a hybrid of optimal control and shape-/topology optimization: set optimal control. This class is integrated into the very abstract …
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Learning sparse representation for image signals
… its high-resolution version. The resulting bilevel optimization problem is solved using a stochastic gradient descent method with the gradient of sparse code found by implicit differentiation. A feed-forward deep neural network motivated by this sparse coding model is designed to further …
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Sparse modeling of high-dimensional data for learning and vision
… dictionary learning algorithm is formulated as a bilevel optimization problem, which we prove can be solved using stochastic gradient descent. Applications of the generic dictionary training algorithm in supervised dictionary training for image classification, super-resolution, and compressive …
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Exploiting structures of trajectory optimization for efficient optimal motion planning
Trajectory optimization is an important tool for optimal motion planning due to its flexibility in cost design, capability to handle complex constraints, and optimality certification. It has been widely used in robotic applications such as autonomous vehicles, unmanned aerial vehicles, humanoid …