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 20 of 26 for “"Online Optimization"”.
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Online optimization problems
In this thesis, we study online optimization problems in routing and allocation applications. Online problems are problems where information is revealed incrementally, and decisions must be made before all information is available. We design and analyze algorithms for a variety of online problems, …
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Online optimization in routing and scheduling
In this thesis we study online optimization problems in routing and scheduling. An online problem is one where the problem instance is revealed incrementally. Decisions can (and sometimes must) be made before all information is available. We design and analyze (polynomial-time) online algorithms …
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Online Optimization for Edge Computing under Uncertainty in Wireless Networks
… performance analysis, and low-complexity optimization of edge computing under the aforementioned uncertainties. First, the problems of edge network formation and task distribution are jointly investigated while considering a hybrid edge-cloud architecture under uncertainty on the arrivals …
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Automated microreactor system for reaction development and online optimization of chemical processes
… efficiency and the accuracy of these reaction optimization investigations through the use of an automated microreactor system. Previous studies have illustrated the benefits of silicon microreactors for the study of chemical reactions. Such advantages include the small reactor volume and the …
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Learning Heterogeneous Resource-Constrained Task Allocation Using Concurrent Multi-Task Bandits
… class of problems, dubbed COncurrent Constrained Online optimization of Allocation (COCOA). The COCOA problem requires online optimization of coalitions in such a way that the unknown rewards of all the tasks are simultaneously maximized using a given multi-robot team with constrained resources. …
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Multi-parametric Programming for Microgrid Operational Scheduling
… and solar resources. This results in a bi-level optimization problem, where choice of the parameterization scheme is made at the upper level while system operation decisions are made at the lower level. The mp-MILP formulation leads to significant improvements in uncertainty management, solution …
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Towards practical neural network meta-modeling
… estimates for practical early stopping of online optimization procedures. Our SRMs are state-of-the-art in performance prediction and early stopping.
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Evolutionary algorithms for compiler-enabled program autotuning
… SiblingRivalry improves on INCREA by optimizing online during a program's execution, dynamically adapting to changes in hardware and the operating system. Continuous adaptation is achieved through racing, where half of available resources are devoted to always-on learning. We evaluate INCREA and …
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Guiding Nonconvex Trajectory Optimization with Hierarchical Graphs of Convex Sets
Collision-free motion planning with trajectory optimization is inherently nonconvex. Some of this nonconvexity is fundamental: the robot might need to make a discrete decision to go left around an obstacle or right around an obstacle. Some of this nonconvexity is potentially more benign: we might …
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Robust and reliable decision-making systems and algorithms
… unreliable components. We consider robustness in online systems and algorithms under the framework of online optimization subject to adversarial perturbations. The framework of online optimization models a rich class of problems from information theory, machine learning, game theory, optimization, …
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Efficient visualization for large-scale and high-dimensional single-cell data
… a low-dimensional space through an efficient online optimization method based on the idea of negative sampling. Using this approach, we can preserve the high-dimensional structure of single-cell data in an embedded low-dimensional space that facilitates visual analyses of the data.
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Digital Control Techniques for Switching Power Converters
… requirements validates the system. Finally, an online optimization method, discrete-time ripple correlation control (DRCC), is shown to automatically operate a switching power converter at an optimal point, such as maximum power from a source. DRCC is derived, stability is proven, and an …
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Essays in network economics
… to each bidder. Nevertheless, by developing new online optimization algorithms, we show how simple mechanisms can approximate the monopolist's optimal revenue. Finally, the third chapter, develops a new model of firm optimization to understand how shrinking electronics have contributed to …
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Online Learning in Control Theory
… to achieve a sublinear regret, similar to the online optimization setting. A modified online Riccati algorithm is introduced that under some uniform boundedness assumptions results in a logarithmic regret bound. In particular, the logarithmic regret for the scalar case is achieved without any …
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New Models qnd Algorithms for Bandits and Markets
… assumption on rewards to regret rates in an online optimization problem, is not fully developed. The primary goal of this dissertation, therefore, will be to fill out the space of models, algorithms, and assumptions used in sequential decision making problems. Toward this end, we will develop …
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Electric Vehicle Fleet Charging Management
… chargers. To tackle this issue, we introduce an online optimization method supported by predictive modeling. This approach integrates future arrival information to accommodate uncertainties concerning arrival times, charge requirements, and due times. Subsequently, a mathematical model is …
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Numerical Methods for Parameter Estimation and Optimal Control of the Red River Network
… equation system, which consists of two nonlinear first-order hyperbolic Partial Differential Equations (PDE) in space and in time. In general a system of equations of this type can not be solved analytically. Therefore I choose a numerical approach, namely the Method Of Lines (MOL) …
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A robust optimization approach to online problems
In this thesis, we consider online optimization problems that are characterized by incrementally revealed input data and sequential irrevocable decisions that must be made without complete knowledge of the future. We employ a combination of mixed integer optimization (MIO) and robust optimization …
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Learning and Optimization in Modern Retail
… topic of this thesis is the application of optimization and statistical inference methods to practical industry problems in the domains of supply chain, demand estimation, assortment optimization, and experimentation. We develop new methodologies that improve the practice of operations …
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A NEW ZEROTH-ORDER ORACLE FOR DISTRIBUTED AND NON-STATIONARY LEARNING
… applied to solve black-box or simulation-based optimization prroblems. These problems arise in many important applications nowa- days, e.g., generating adversarial attacks on machine learning systems, learning to control the system with complicated physics structure or human in the loop. In …
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