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 49 for “"Optimization Theory"”.
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Adaptable optimization : theory and algorithms
Optimization under uncertainty is a central ingredient for analyzing and designing systems with incomplete information. This thesis addresses uncertainty in optimization, in a dynamic framework where information is revealed sequentially, and future decisions are adaptable, i.e., they depend …
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Convexity and duality in optimization theory
Thesis. 1977. Ph.D.--Massachusetts Institute of Technology. Dept. of Mathematics.
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Shape optimization theory and applications in hydrodynamics
… Lagrange multiplier theorem and optimal control theory are applied to a continuous shape optimization problem for reducing the wave resistance of a submerged body translating at a steady forward velocity well below a free surface. In the latter approach, when the constraint formed by the boundary …
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Optimization Theory and Machine Learning Practice: Mind the Gap
… a good model based on a known dataset requires optimization. In particular, an optimization procedure generates a variable in a constraint set to minimize an objective. This process subsumes many machine learning pipelines including neural network training, which will be our main testing ground …
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New variational principles with applications to optimization theory and algorithms
… some applications of variational analysis in optimization theory and algorithms. In the first part we develop new extremal principles in variational analysis that deal with finite and infinite systems of convex and nonconvex sets. The results obtained, under the name of tangential extremal …
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Bridging the Gap: From Artificial Intelligence and Optimization Theory to Action
… how methodological advancements can bridge this theory-practice divide while maintaining rigorous theoretical foundations and guarantees. In the first part, we focus on optimization methodologies that scale traditional OR approaches to handle real-world problem sizes and uncertainty. In Chapter …
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A Novel Machine Learning Approach to Robust Optimization: Theory and Applications
… based approaches to estimate parameters of optimization models, enabling more informed decisions. However, these models often inherit uncertainty from the data they are trained on, leading to unreliable decisions when deployed at face value. This body of work develops robust optimization …
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Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software
… frameworks for nonlinear, nonconvex optimization problems in statistics, machine learning, and optimal control. Disciplined Geodesically Convex Programming (DGCP) extends convexity verification to Riemannian manifolds, enabling optimization on curved spaces with global optimality …
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Integrating Optimization and Modern Machine Learning: Theory, Computation, and Healthcare Applications
Optimization and machine learning are two predominant fields for decision-making today. The increasing availability of data over the past years has facilitated advancements in the intersection of these two domains, which in turn has led to better decision support tools. Optimization has …
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Capacity analysis, cycle time optimization, and supply chain strategy in multi-product biopharmaceutical manufacturing operations
Application of system optimization theory, supply chain principles, and capacity modeling are increasingly valuable tools for use in pharmaceutical manufacturing facilities. The dynamics of the pharmaceutical industry - market exclusivity, high margins, product integrity and contamination …
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Methods for Solving Generalized Systems of Inequalities With Application to Nonlinear Programming
… induced on Y by K. A wide variety of problems in optimization theory can be cast in this framework, e.g. solving systems of equations and inequalities, solving general nonlinear complementarity problems, and finding Karush-Kuhn-Tucker points for mathematical programs.
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Optimal control strategies for saccadic eye movements in humans
… systems: open and closed loop control, dynamic optimization, internal models, and learning. In this dissertation, aspects of these various approaches will be utilized within the context of the human eye system. After establishing the mathematical description of ocular dynamics we shall explore …
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Análise limite de cascas via otimização
… Limit analysis is essentially a problem in optimization. Therefore the theory of plastic collapse is presented by means of optimization theory and also the proposed methods are based on mathematical programming algorithms. Constitutive equations are established for generalized variables as …
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Rf Low Pass Filter Design And Fabrication Using Integrated Passive Device Technology
… design and processing parameters for design optimization of both the inductors and IPD harmonic filters. The effective use of EM simulation enables us to realize the successful development of high performance harmonic filters. To make the optimization be more flexible and also for a deeper …
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TOWARDS QUANTUM ADVANTAGE AND CERTIFICATION WITH NOISY INTERMEDIATE-SCALE QUANTUM DEVICES
… physics, quantum chemistry, combinatorial optimization and machine learning. This thesis discusses noisy intermediate-scale quantum algorithms for quantum linear system problem, closed system simulation, and open system simulation. Our algorithms for simulating dynamics do not have a …
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Nonlinear contraction tools for constrained optimization
… contraction analysis, a recent stability theory for nonlinear systems, and constrained optimization theory. Although dynamic systems and optimization are both areas that have been extensively studied [21], few results have been achieved in this direction because strong enough tools for …
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A Systems Theoretic Framework for Online Machine Learning with an Empirical Application
… widely explored in terms of statistical learning theory, convex optimization theory and game theory, however, little to no frameworks exist for the design and application of online learning systems, both in theory and in practice. This work presents a unique, general framework for the modeling of …
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General -Purpose Processors for Multimedia Applications: Predictability and Energy Efficiency
… Our final algorithm is based on formal optimization theory, which lends a key advantage over previously proposed algorithms: it requires little tuning of its design parameters for an actual implementation. Our final algorithm is predictive---its decisions are made using predictions about …
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Stochastic modeling and optimization of petroleum exploration portfolios
… exploration company must employ a portfolio optimization strategy in order to determine the best combination of available prospects to pursue. Today, exploration teams can extensively model their portfolios using industry-standard platforms capable of aggregating expected volumes and risks …
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Design of compact intermediate heat exchangers for gas cooled fast reactors
… permit a net cycle efficiency of at least 40%. Optimization theory, sensitivity studies, and thermal-hydraulic constraints contributed to shaping the final design. The friction factor analysis showed that the correlations cited in the literature overestimate the value by approximately a factor …
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