Relevant bibliographies by topics / First Order Optimization Methods / Dissertations / Theses
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Dissertations / Theses on the topic 'First Order Optimization Methods'

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Published: 5 June 2025
Last updated: 15 July 2025

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1

Lan, Guanghui. "Convex optimization under inexact first-order information." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29732.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Arkadi Nemirovski; Committee Co-Chair: Alexander Shapiro; Committee Co-Chair: Renato D. C. Monteiro; Committee Member: Anatoli Jouditski; Committee Member: Shabbir Ahmed. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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2

Barré, Mathieu. "Worst-case analysis of efficient first-order methods." Electronic Thesis or Diss., Université Paris sciences et lettres, 2021. http://www.theses.fr/2021UPSLE064.

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De nombreuses applications modernes reposent sur la résolution de problèmes d’optimisations (par exemple, en biologie numérique, en mécanique, en finance), faisant des méthodes d’optimisation des outils essentiels dans de nombreux domaines scientifiques. Apporter des garanties sur le comportement de ces méthodes constitue donc un axe de recherche important. Une façon classique d’analyser un algorithme d’optimisation consiste à étudier son comportement dans le pire cas. C'est-à-dire, donner des garanties sur son comportement (par exemple sa vitesse de convergence) qui soient indépendantes de la
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3

Silveti, Falls Antonio. "First-order noneuclidean splitting methods for large-scale optimization : deterministic and stochastic algorithms." Thesis, Normandie, 2021. http://www.theses.fr/2021NORMC204.

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Dans ce travail, nous développons et examinons deux nouveaux algorithmes d'éclatement du premier ordre pour résoudre des problèmes d'optimisation composites à grande échelle dans des espaces à dimensions infinies. Ces problèmes sont au coeur de nombres de domaines scientifiques et d'ingénierie, en particulier la science des données et l'imagerie. Notre travail est axé sur l'assouplissement des hypothèses de régularité de Lipschitz généralement requises par les algorithmes de fractionnement du premier ordre en remplaçant l'énergie euclidienne par une divergence de Bregman. Ces développements pe
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4

Khirirat, Sarit. "Randomized first-order methods for convex optimization : Improved convergence rate bounds and experimental evaluations." Thesis, KTH, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214697.

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Huge-scale optimization problems appear in several applications ranging frommachine learning over large data sets to distributed model predictive control.Classical optimization algorithms struggle to handle these large-scale computations,and recently, a number of randomized rst-order methods that are simpleto implement and have small per-iteration cost have been proposed. However,optimal step size selections and corresponding convergence rates of many randomizedrst-order methods were still unknown. In this thesis, we hence deriveconvergence rate results for several randomized rst-order methods
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Van, Mai Vien. "Large-Scale Optimization With Machine Learning Applications." Licentiate thesis, KTH, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263147.

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This thesis aims at developing efficient algorithms for solving some fundamental engineering problems in data science and machine learning. We investigate a variety of acceleration techniques for improving the convergence times of optimization algorithms.  First, we investigate how problem structure can be exploited to accelerate the solution of highly structured problems such as generalized eigenvalue and elastic net regression. We then consider Anderson acceleration, a generic and parameter-free extrapolation scheme, and show how it can be adapted to accelerate practical convergence of proxi
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6

He, Niao. "Saddle point techniques in convex composite and error-in-measurement optimization." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54400.

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This dissertation aims to develop efficient algorithms with improved scalability and stability properties for large-scale optimization and optimization under uncertainty, and to bridge some of the gaps between modern optimization theories and recent applications emerging in the Big Data environment. To this end, the dissertation is dedicated to two important subjects -- i) Large-scale Convex Composite Optimization and ii) Error-in-Measurement Optimization. In spite of the different natures of these two topics, the common denominator, to be presented, lies in their accommodation for systematic
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Ghadimi, Euhanna. "Accelerating Convergence of Large-scale Optimization Algorithms." Doctoral thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-162377.

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Several recent engineering applications in multi-agent systems, communication networks, and machine learning deal with decision problems that can be formulated as optimization problems. For many of these problems, new constraints limit the usefulness of traditional optimization algorithms. In some cases, the problem size is much larger than what can be conveniently dealt with using standard solvers. In other cases, the problems have to be solved in a distributed manner by several decision-makers with limited computational and communication resources. By exploiting problem structure, however, i
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8

Sforni, Lorenzo. "A First-Order Closed-loop Methodology for Nonlinear Optimal Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21429/.

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This thesis is focused on state-of-art numerical optimization methods for nonlinear (discrete-time) optimal control. These challenging problems arise when dealing with complex tasks for autonomous systems (e.g. vehicles or robots) which require the generation of a trajectory that satisfies the system dynamics and, possibly, input and state constraints due to, e.g, actuator limits or safety region of operation. A general formulation is proposed that allows the implementation of different descent optimization algorithms on optimal control problems exploiting the beneficial effects of state f
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9

Clark, Patrick Ryan. "Reliability-Based Topology Optimization with Analytic Sensitivities." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78665.

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It is a common practice when designing a system to apply safety factors to the critical failure load or event. These safety factors provide a buffer against failure due to the random or un-modeled behavior, which may lead the system to exceed these limits. However these safety factors are not directly related to the likelihood of a failure event occurring. If the safety factors are poorly chosen, the system may fail unexpectedly or it may have a design which is too conservative. Reliability-Based Design Optimization (RBDO) is an alternative approach which directly considers the likelihood of f
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10

Alli-Oke, Razak Olusegun. "Robustness and optimization in anti-windup control." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/robustness-and-optimization-in-antiwindup-control(8b98c920-90c3-4fbc-95a8-0cc7ae2a607a).html.

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This thesis is broadly concerned with online-optimizing anti-windup control. These are control structures that implement some online-optimization routines to compensate for the windup effects in constrained control systems. The first part of this thesis examines a general framework for analyzing robust preservation in anti-windup control systems. This framework - the robust Kalman conjecture - is defined for the robust Lur’e problem. This part of the thesis verifies this conjecture for first-order plants perturbed by various norm-bounded unstructured uncertainties. Integral quadratic constrain
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11

Kilinc-Karzan, Fatma. "Tractable relaxations and efficient algorithmic techniques for large-scale optimization." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41141.

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In this thesis, we develop tractable relaxations and efficient algorithms for large-scale optimization. Our developments are motivated by a recent paradigm, Compressed Sensing (CS), which consists of acquiring directly low-dimensional linear projections of signals, possibly corrupted with noise, and then using sophisticated recovery procedures for signal reconstruction. We start by analyzing how to utilize a priori information given in the form of sign restrictions on part of the entries. We propose necessary and sufficient on the sensing matrix for exact recovery of sparse signals, utilize th
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12

Ait, El Faqir Marouane. "Prédiction de la structure de contrôle de bactéries par optimisation sous incertitude." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC036/document.

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L'approche de la biologie des systèmes vise à intégrer les méthodologies appliquées dans la conception et l'analyse des systèmes technologiques complexes, au sein de la biologie afin de comprendre les principes de fonctionnement globaux des systèmes biologiques. La thèse s'inscrit dans le cadre de la biologie des systèmes et en particulier dans la prolongation d'une méthode issue de ce cadre : la méthode Resource Blance Analysis (RBA). Nous visons dans cette thèse à augmenter le pouvoir prédictif de la méthode via un travail de modélisation tout en gardant un bon compromis entre représentativi
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13

Mansour, Rami. "Reliability Assessment and Probabilistic Optimization in Structural Design." Doctoral thesis, KTH, Hållfasthetslära (Avd.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183572.

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Research in the field of reliability based design is mainly focused on two sub-areas: The computation of the probability of failure and its integration in the reliability based design optimization (RBDO) loop. Four papers are presented in this work, representing a contribution to both sub-areas. In the first paper, a new Second Order Reliability Method (SORM) is presented. As opposed to the most commonly used SORMs, the presented approach is not limited to hyper-parabolic approximation of the performance function at the Most Probable Point (MPP) of failure. Instead, a full quadratic fit is use
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14

Chen, Xi. "Learning with Sparcity: Structures, Optimization and Applications." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/228.

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The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques p
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15

Mengesha, Abi Taddesse. "Characterizing phosphate desorption kinetics from soil : an approach to predicting plant available phosphorus." Thesis, University of Pretoria, 2008. http://hdl.handle.net/2263/24346.

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Many agricultural fields that have received long-term applications of P often contain levels of P exceeding those required for optimal crop production. Knowledge of the effect of the P remaining in the soil (residual effect) is of great importance for fertilization management. In order to characterize P forms in soils, a wide variety of methods have been proposed. The use of dialysis membrane tubes filled with hydrous ferric oxide (DMT-HFO) has recently been reported as an effective way to characterize P desorption over a long-term in laboratoty studies. However, there is little information on
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16

Le, Manh Hung. "Études mathématiques et numériques de la complémentarité aux valeurs propres et des problèmes d'accélération dans l'optimisation du premier ordre." Electronic Thesis or Diss., Limoges, 2023. http://www.theses.fr/2023LIMO0104.

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Dans cette thèse, j’explore deux sujets clés. Premièrement, je m’intéresse à l’étude mathématique et numérique du problème de complémentarité des valeurs propres de Pareto et de sa contrepartie inverse. Notre approche utilise des méthodes de points intérieurs, complétées par une technique de lissage non paramétrique. L’efficacité des méthodologies proposées est soulignée par un ensemble d’expériences numériques. En mettant l’accent sur l’optimisation continue, nous adoptons une perspective de systèmes dynamiques. Plus précisément, nous étudions divers algorithmes inertiels à gradient proximal,
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17

Shahtaheri, Yasaman. "A Probabilistic Decision Support System for a Performance-Based Design of Infrastructures." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/96804.

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Infrastructures are the most fundamental facilities and systems serving the society. Due to the existence of infrastructures in economic, social, and environmental contexts, all lifecycle phases of such fundamental facilities should maximize utility for the designers, occupants, and the society. With respect to the nature of the decision problem, two main types of uncertainties may exist: 1) the aleatory uncertainty associated with the nature of the built environment (i.e., the economic, social, and environmental impacts of infrastructures must be described as probabilistic); and 2) the episte
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18

Chen, Annie I.-An. "Fast distributed first-order methods." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75628.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 91-94).<br>This thesis provides a systematic framework for the development and analysis of distributed optimization methods for multi-agent networks with time-varying connectivity. The goal is to optimize a global objective function which is the sum of local objective functions privately known to individual agents. In our methods, each agent iteratively updates its estimate of the global optimum by
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19

Strogies, Nikolai. "Optimization of nonsmooth first order hyperbolic systems." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17633.

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Wir betrachten Optimalsteuerungsprobleme, die von partiellen Differentialgleichungen beziehungsweise Variationsungleichungen mit Differentialoperatoren erster Ordnung abhängen. Wir führen die Reformulierung eines Tagebauplanungsproblems, das auf stetigen Funktionen beruht, ein. Das Resultat ist ein Optimalsteuerungsproblem für Viskositätslösungen einer Eikonalgleichung. Die Existenz von Lösungen dieses und bestimmter Hilfsprobleme, die von semilinearen PDG‘s mit künstlicher Viskosität abhängen, wird bewiesen, Stationaritätsbedingungen hergeleitet und ein schwaches Konsistenzresultat für statio
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20

Fountoulakis, Kimon. "Higher-order methods for large-scale optimization." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/15797.

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There has been an increased interest in optimization for the analysis of large-scale data sets which require gigabytes or terabytes of data to be stored. A variety of applications originate from the fields of signal processing, machine learning and statistics. Seven representative applications are described below. - Magnetic Resonance Imaging (MRI): A medical imaging tool used to scan the anatomy and the physiology of a body. - Image inpainting: A technique for reconstructing degraded parts of an image. - Image deblurring: Image processing tool for removing the blurriness of a photo caused by
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21

Liang, Jingwei. "Convergence rates of first-order operator splitting methods." Caen, 2016. http://www.theses.fr/2016CAEN2024.

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Ce manuscrit traite de l’analyse de convergence des méthodes du premier ordre d’éclatement d’opérateurs qui sont omniprésents en optimisation non-lisse moderne. Il consiste en trois avancées théoriques principales sur la caractérisation des cette classe de méthodes, à savoir: leur taux de convergence globaux, de nouveaux schémas d’éclatement et une analyse de leur convergence linéaire locale. Dans un premier temps, nous proposons des taux de convergence globaux (sous-linéaires) et locaux (linéaire) pour l’itération de Krasnosel’ski˘ı-Mann inexacte, et ses applications à un large éventail de sc
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22

Zhang, Jingzhao S. M. Massachusetts Institute of Technology. "Dynamical systems view of acceleration in first order optimization." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122886.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 85-87).<br>Gradient based optimization algorithms are among the most fundamental algorithms in optimization and machine learning, yet they suffer from slow convergence. Consequently, accelerating gradient based methods have become an important recent topic of study. In this thesis, we focus on explaining and understanding the acceleration results. In particular, we aim to provide insights into
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Beren, Eric B. "Methods for optimization based fixed-order control design /." Online version, 1997. http://bibpurl.oclc.org/web/29659.

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24

Amengual, Gari Sebastia Vicenc. "Alternative methods to generate a first-order directional microphone." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26843.

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This thesis deals with the implementation and analysis of diferent methods to generate a first-order directional microphone for source location and noise measurement purposes. The methods analysed include a cardioid capsule, and virtual methods such the combination of the signals of two omnidirectional microphones, the combination of the signals of an omnidirectional and a figure of 8 microphones, as well as a Soundfield microphone which is based on the Ambisonics principle. The frequency response function, directivity error and signal-to-noise ratio of the resulting first-order directional mi
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Kudo, Jun S. M. Massachusetts Institute of Technology. "Robust adaptive high-order RANS methods." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95563.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 89-94).<br>The ability to achieve accurate predictions of turbulent flow over arbitrarily complex geometries proves critical in the advancement of aerospace design. However, quantitatively accurate results from modern Computational Fluid Dynamics (CFD) tools are often accompanied by intractably high computational expenses and are significantly hindered by the lack of automation. In particular, the gene
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COLI, Vanna Lisa. "Variable metric first–order methods for applications in biomedical imaging." Doctoral thesis, Università degli studi di Ferrara, 2018. http://hdl.handle.net/11392/2488278.

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Lo scopo di questo lavoro è l’analisi di alcune tecniche di accelerazione per metodi del primo ordine per l’ottimizzazione vincolata non lineare e discuterne le prestazioni nell’ambito di problemi di imaging biomedicale. Le strategie proposte sfruttano recenti idee basate su opportune scelte dei parametri di steplength e sull'introduzione di una metrica variabile indotta da matrici di scaling nella definizione del passo di proiezione del gradiente; tali strategie contribuiscono in maniera efficace nell'aumento della velocità di convergenza dei metodi senza aggiungere significativi costi compu
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Patil, Vinay. "Methods for Wave Generation in an Order Picking Warehouse." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1181138535.

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28

Voisei, Mircea D. "First-Order Necessary Optimality Conditions for Nonlinear Optimal Control Problems." Ohio University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1091111473.

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Voisei, Mircea Dan. "First-order necessary optimality conditions for nonlinar optimal control problems." Ohio : Ohio University, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1091111473.

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30

Khirirat, Sarit. "First-Order Algorithms for Communication Efficient Distributed Learning." Licentiate thesis, KTH, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263738.

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Technological developments in devices and storages have made large volumes of data collections more accessible than ever. This transformation leads to optimization problems with massive data in both volume and dimension. In response to this trend, the popularity of optimization on high performance computing architectures has increased unprecedentedly. These scalable optimization solvers can achieve high efficiency by splitting computational loads among multiple machines. However, these methods also incur large communication overhead. To solve optimization problems with millions of parameters,
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31

Yue, Hongwei. "First-order affine scaling continuous method for convex quadratic programming." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/39.

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We develop several continuous method models for convex quadratic programming (CQP) problems with di.erent types of constraints. The essence of the continuous method is to construct one ordinary di.erential equation (ODE) system such that its limiting equilibrium point corresponds to an optimal solution of the underlying optimization problem. All our continuous method models share the main feature of the interior point methods, i.e., starting from any interior point, all the solution trajectories remain in the interior of the feasible regions. First, we present an a.ne scaling continuous metho
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Irvin, Lance J. "Parallelization and optimization of higher-order Finite-difference methods utilizing MPI." Thesis, Wichita State University, 2010. http://hdl.handle.net/10057/3724.

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Parallelization of computational models is key to utilizing the widely available computational resources of today to solve problems in ways the computational science community could not yesterday. The main focus of the work presented is in the parallelization of a Direct Numerical Simulation (DNS) nite-di erence Computational Fluid Dynamics (CFD) ow solver based on the Weighted Essentially Non-Oscillatory (WENO) scheme. The parallel DNS model was developed by modifying the existing serial model developed by Arshed Ghulam. An Alternating Direction Implicit (ADI) nite-di erence heat solver is
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33

Quick, David Arthur. "!-Logic : first order reasoning for families of non-commutative string diagrams." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:baf2d50d-8c5f-419d-9b3d-f2f700f8acbd.

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Equational reasoning with string diagrams provides an intuitive method for proving equations between morphisms in various forms of monoidal category. !-Graphs were introduced with the intention of reasoning with infinite families of string diagrams by allowing repetition of sub-diagrams. However, their combinatoric nature only allows commutative nodes. The aim of this thesis is to extend the !-graph formalism to remove the restriction of commutativity and replace the notion of equational reasoning with a natural deduction system based on first order logic. The first major contribution is the s
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Zachariadis, Zacharias Ioannis. "High resolution and high order methods for RANS modelling and aerodynamic optimization." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/3806.

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With the optimisation of fixed aerodynamic shapes reaching its limits, the active flow control concept increasingly attracts attention of both academia and industry. Adaptive wing technology, and shape morphing airfoils in particular, represents a promising way forward. The aerodynamic performance of the morphing profiles is an important issue affecting the overall aerodynamic performance of an adaptive wing. A new concept of active flow, the Active Camber concept has been investigated. The actuator is integrated into the aerofoil and aerofoil morphing is realized via camber deformation. In or
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Stanistreet, Timothy Francis. "Numerical methods for first order partial differential equations describing steady-state forming processes." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398232.

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Zigic, Jovan. "Optimization Methods for Dynamic Mode Decomposition of Nonlinear Partial Differential Equations." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103862.

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Reduced-order models have long been used to understand the behavior of nonlinear partial differential equations. Naturally, reduced-order modeling techniques come at the price of either computational accuracy or computation time. Optimization techniques are studied to improve either or both of these objectives and decrease the total computational cost of the problem. This thesis focuses on the dynamic mode decomposition (DMD) applied to nonlinear PDEs with periodic boundary conditions. It provides one study of an existing optimization framework for the DMD method known as the Optimized DMD and
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Narisetty, Murali Krishna. "Methods in order batching for picking in an order picking distribution center." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174920072.

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38

Cabezas, García José Xavier. "Heuristic methods for solving two discrete optimization problems." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31093.

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In this thesis we study two discrete optimization problems: Traffic Light Synchronization and Location with Customers Orderings. A widely used approach to solve the synchronization of traffic lights on transport networks is the maximization of the time during which cars start at one end of a street and can go to the other without stopping for a red light (bandwidth maximization). The mixed integer linear model found in the literature, named MAXBAND, can be solved by optimization solvers only for small instances. In this manuscript we review in detail all the constraints of the original linear
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Lieder, Felix [Verfasser]. "Projection Based Methods for Conic Linear Programming — Optimal First Order Complexities and Norm Constrained Quasi Newton Methods / Felix Lieder." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2018. http://d-nb.info/1166398277/34.

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Zhang, Lin. "Large-Scale Simulations Using First and Second Order Adjoints with Applications in Data Assimilation." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33727.

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In large-scale air quality simulations we are interested in the influence factors which cause changes of pollutants, and optimization methods which improve forecasts. The solutions to these problems can be achieved by incorporating adjoint models, which are efficient in computing the derivatives of a functional with respect to a large number of model parameters. In this research we employ first order adjoints in air quality simulations. Moreover, we explore theoretically the computation of second order adjoints for chemical transport models, and illustrate their feasibility in several aspects
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Yuan, Rui. "Stochastic Second Order Methods and Finite Time Analysis of Policy Gradient Methods." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT010.

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Pour résoudre les problèmes de machine learning à grande échelle, les méthodes de premier ordre telles que la descente du gradient stochastique et l'ADAM sont les méthodes de choix en raison de leur coût pas cher par itération. Le problème des méthodes du premier ordre est qu'elles peuvent nécessiter un réglage important des paramètres et/ou une connaissance des paramètres du problème. Il existe aujourd'hui un effort considérable pour développer des méthodes du second ordre stochastiques efficaces afin de résoudre des problèmes de machine learning à grande échelle. La motivation est qu'elles d
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Pereira, Yuri Rafael Leite. "Methods for vector optimization: trust region and proximal on riemannian manifolds and Newton with variable order." Universidade Federal de Goiás, 2017. http://repositorio.bc.ufg.br/tede/handle/tede/7791.

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Strogies, Nikolai Verfasser], Andreas [Gutachter] [Griewank, Felipe [Gutachter] Alvarez, and Carlos [Gutachter] Rautenberg. "Optimization of nonsmooth first order hyperbolic systems : theory and application / Nikolai Strogies ; Gutachter: Andreas Griewank, Felipe Alvarez, Carlos Rautenberg." Berlin : Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://d-nb.info/1119861497/34.

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44

Strogies, Nikolai [Verfasser], Andreas [Gutachter] Griewank, Felipe [Gutachter] Alvarez, and Carlos [Gutachter] Rautenberg. "Optimization of nonsmooth first order hyperbolic systems : theory and application / Nikolai Strogies ; Gutachter: Andreas Griewank, Felipe Alvarez, Carlos Rautenberg." Berlin : Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://d-nb.info/1119861497/34.

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45

Owens, Alex. "Discontinuous isogeometric analysis methods for the first order form of the neutron transport equation with discrete ordinate angular discretisation." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/52924.

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This thesis presents the development of a variety of discontinuous isogeometric discretisations of the discrete ordinates equations for neutron transport in two spatial dimensions. Three discretisations are presented of increasing sophistication, and their convergence properties analysed for a wide selection of test cases. The first discretisation uses a conforming mesh approach, which is analogous to many existing discontinuous Galerkin finite element methods for the discrete ordinates equations. This simplifies the analysis of the differences between isogeometric and finite element methods i
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46

Schmidt, Andreas [Verfasser], and Hans Georg [Akademischer Betreuer] Bock. "Direct Methods for PDE-Constrained Optimization Using Derivative-Extended POD Reduced-Order Models / Andreas Schmidt ; Betreuer: Hans Georg Bock." Heidelberg : Universitätsbibliothek Heidelberg, 2014. http://d-nb.info/118030070X/34.

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Papoutsellis, Evangelos. "First-order gradient regularisation methods for image restoration : reconstruction of tomographic images with thin structures and denoising piecewise affine images." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/256216.

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The focus of this thesis is variational image restoration techniques that involve novel non-smooth first-order gradient regularisers: Total Variation (TV) regularisation in image and data space for reconstruction of thin structures from PET data and regularisers given by an infimal-convolution of TV and $L^p$ seminorms for denoising images with piecewise affine structures. In the first part of this thesis, we present a novel variational model for PET reconstruction. During a PET scan, we encounter two different spaces: the sinogram space that consists of all the PET data collected from the det
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Ankelhed, Daniel. "On design of low order H-infinity controllers." Doctoral thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-67869.

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When designing controllers with robust performance and stabilization requirements, H-infinity synthesis is a common tool to use. These controllers are often obtained by solving mathematical optimization problems. The controllers that result from these algorithms are typically of very high order, which complicates implementation. Low order controllers are usually desired, since they are considered more reliable than high order controllers. However, if a constraint on the maximum order of the controller is set that is lower than the order of the so-called augmented system, the optimization probl
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Sticksel, Christoph. "Efficient equational reasoning for the Inst-Gen Framework." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/efficient-equational-reasoning-for-the-instgen-framework(67ead780-3ec9-4897-ac17-48d3de011b4b).html.

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We can classify several quite different calculi for automated reasoning in first-order logic as instantiation-based methods (IMs). Broadly speaking, unlike in traditional calculi such as resolution where the first-order satisfiability problem is tackled by deriving logical conclusions, IMs attempt to reduce the first-order satisfiability problem to propositional satisfiability by intelligently instantiating clauses. The Inst-Gen-Eq method is an instantiation-based calculus which is complete for first-order clause logic modulo equality. Its distinctive feature is that it combines first-order re
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Schnücke, Gero [Verfasser], Christian [Gutachter] Klingenberg, and Manfred [Gutachter] Dobrowolski. "Arbitrary Lagrangian-Eulerian Discontinous Galerkin methods for nonlinear time-dependent first order partial differential equations / Gero Schnücke ; Gutachter: Christian Klingenberg, Manfred Dobrowolski." Würzburg : Universität Würzburg, 2016. http://d-nb.info/1117477290/34.

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