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Rozprawy doktorskie na temat „Convex optimization”

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Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Joulin, Armand. "Convex optimization for cosegmentation." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00826236.

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La simplicité apparente avec laquelle un humain perçoit ce qui l'entoure suggère que le processus impliqué est en partie mécanique, donc ne nécessite pas un haut degré de réflexion. Cette observation suggère que notre perception visuelle du monde peut être simulée sur un ordinateur. La vision par ordinateur est le domaine de recherche consacré au problème de la création d'une forme de perception visuelle pour des ordinateurs. La puissance de calcul des ordinateurs des années 50 ne permettait pas de traiter et d'analyser les données visuelles nécessaires à l'élaboration d'une perception visuell

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2

Rätsch, Gunnar. "Robust boosting via convex optimization." Phd thesis, Universität Potsdam, 2001. http://opus.kobv.de/ubp/volltexte/2005/39/.

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In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, bei dem die resultierende Klassifikations- oder Regressionsregel aus einfachen Regeln - den Basishypothesen - zusammengesetzt ist. Die sogenannten Boosting Algorithmen erzeugen iterativ eine gewichtete Summe von Basishypothesen, die gut auf ungesehenen Mustern vorhersagen. <br /> Die Arbeit behandelt

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3

Nekooie, Batool. "Convex optimization involving matrix inequalities." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/13880.

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Jangam, Ravindra nath vijay kumar. "BEAMFORMING TECHNIQUES USING CONVEX OPTIMIZATION." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-33934.

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The thesis analyses and validates Beamforming methods using Convex Optimization. CVX which is a Matlab supported tool for convex optimization has been used to develop this concept. An algorithm is designed by which an appropriate system has been identified by varying parameters such as number of antennas, passband width, and stopbands widths of a beamformer. We have observed the beamformer by minimizing the error for Least-square and Infinity norms. A graph obtained by the optimum values between least-square and infinity norms shows us a trade-off between these two norms. We have observed con

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5

Saunderson, James (James Francis). "Subspace identification via convex optimization." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66475.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 88-92).<br>In this thesis we consider convex optimization-based approaches to the classical problem of identifying a subspace from noisy measurements of a random process taking values in the subspace. We focus on the case where the measurement noise is component-wise independent, known as the factor analysis model in statistics. We develop a new analysis of an existing convex optimization-based heur

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6

Shewchun, John Marc 1972. "Constrained control using convex optimization." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/46471.

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7

Boţ, Radu Ioan. "Conjugate duality in convex optimization." Berlin [u.a.] Springer, 2010. http://dx.doi.org/10.1007/978-3-642-04900-2.

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8

Aggarwal, Varun. "Analog circuit optimization using evolutionary algorithms and convex optimization." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40525.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Includes bibliographical references (p. 83-88).<br>In this thesis, we analyze state-of-art techniques for analog circuit sizing and compare them on various metrics. We ascertain that a methodology which improves the accuracy of sizing without increasing the run time or the designer effort is a contribution. We argue that the accuracy of geometric programming can be improved without adversely influencing the run time or increasing the designer's effort. This is facilitated by dec

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9

van, den Berg Ewout. "Convex optimization for generalized sparse recovery." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/16646.

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The past decade has witnessed the emergence of compressed sensing as a way of acquiring sparsely representable signals in a compressed form. These developments have greatly motivated research in sparse signal recovery, which lies at the heart of compressed sensing, and which has recently found its use in altogether new applications. In the first part of this thesis we study the theoretical aspects of joint-sparse recovery by means of sum-of-norms minimization, and the ReMBo-l₁ algorithm, which combines boosting techniques with l₁-minimization. For the sum-of-norms approach we derive necessary

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10

Lin, Chin-Yee. "Interior point methods for convex optimization." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/15044.

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11

Niewoehner, Robert Jay. "Plant/controller optimization by convex methods." Thesis, Monterey, California. Naval Postgraduate School, 1994. http://hdl.handle.net/10945/28453.

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Approved for public release; distribution is unlimited<br>This report presents results of a three phase effort to demonstrate the use of convex control design techniques in aeronautical applications. The first phase was the demonstration of a methodology by which classical aircraft controller design requirements could be translated into the weighting matrices for H infinity controller synthesis. The second phase extended that methodology to the design of mixed H2 / H infinity controllers. The third phase considered the problem of minimizing the size of aircraft control surfaces while meeting c

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12

Dautbegovic, Dino. "Convex Optimization Methods for System Identification." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34808.

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The extensive use of a least-squares problem formulation in many fields is partly motivated by the existence of an analytic solution formula which makes the theory comprehensible and readily applicable, but also easily embedded in computer-aided design or analysis tools. While the mathematics behind convex optimization has been studied for about a century, several recent researches have stimulated a new interest in the topic. Convex optimization, being a special class of mathematical optimization problems, can be considered as generalization of both least-squares and linear programming. As in

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13

Sou, Kin Cheong 1979. "Convex optimization methods for model reduction." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45872.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>Includes bibliographical references (p. 153-161).<br>Model reduction and convex optimization are prevalent in science and engineering applications. In this thesis, convex optimization solution techniques to three different model reduction problems are studied.Parameterized reduced order modeling is important for rapid design and optimization of systems containing parameter dependent reducible sub-circuits such as interconnects and RF inductors. The first part of the thesis pre

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14

Modiba, Jacob Mantjitji. "An overview of sparse convex optimization." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/64352.

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Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. Optimization is seeking values of a variable that leads to an optimal value of the function that is to be optimized. Suppose we have a system of equations where there more unknowns than the equations. This type of system leads to an infinitely many solution. If one has prior knowledge that the solution is sparse this problem can be treated as an optimization problem. In this mini-dissertation we will discuss the convex algorithms for finding sparse solution. We use convex algorithm are cho

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15

Lewis, Naama. "ITEM RESPONSE MODELS AND CONVEX OPTIMIZATION." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1782.

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Item Response Theory (IRT) Models, like the one parameter, two parameters, or normal Ogive, have been discussed for many years. These models represent a rich area of investigation due to their complexity as well as the large amount of data collected in relationship to model parameter estimation. Here we propose a new way of looking at IRT models using I-projections and duality. We use convex optimization methods to derive these models. The Kullback-Leibler divergence is used as a metric and specific constraints are proposed for the various models. With this approach, the dual problem is s

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16

Fontaine, Xavier. "Sequential learning and stochastic optimization of convex functions." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM024.

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Dans cette thèse nous étudions plusieurs problèmes d'apprentissage automatique qui sont tous liés à la minimisation d'une fonction bruitée, qui sera souvent convexe.Du fait de leurs nombreuses applications nous nous concentrons sur des problèmes d'apprentissage séquentiel, qui consistent à traiter des données ``à la volée'', ou en ligne.La première partie de cette thèse est ainsi consacrée à l'étude de trois différents problèmes d'apprentissage séquentiel dans lesquels nous rencontrons le compromis classique ``exploration vs. exploitation''.Dans chacun de ces problèmes un agent doit prendre de

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17

Inacio, Helder. "Convex relaxations for cubic polynomial problems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47563.

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This dissertation addresses optimization of cubic polynomial problems. Heuristics for finding good quality feasible solutions and for improving on existing feasible solutions for a complex industrial problem, involving cubic and pooling constraints among other complicating constraints, have been developed. The heuristics for finding feasible solutions are developed based on linear approximations to the original problem that enforce a subset of the original problem constraints while it tries to provide good approximations for the remaining constraints, obtaining in this way nearly feasible s

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18

Guzman, Paredes Cristobal. "Information, complexity and structure in convex optimization." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53577.

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This thesis is focused on the limits of performance of large-scale convex optimization algorithms. Classical theory of oracle complexity, first proposed by Nemirovski and Yudin in 1983, successfully established the worst-case behavior of methods based on local oracles (a generalization of first-order oracle for smooth functions) for nonsmooth convex minimization, both in the large-scale and low-scale regimes; and the complexity of approximately solving linear systems of equations (equivalent to convex quadratic minimization) over Euclidean balls, under a matrix-vector multiplication oracle. O

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19

Hendrich, Christopher. "Proximal Splitting Methods in Nonsmooth Convex Optimization." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-149548.

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This thesis is concerned with the development of novel numerical methods for solving nondifferentiable convex optimization problems in real Hilbert spaces and with the investigation of their asymptotic behavior. To this end, we are also making use of monotone operator theory as some of the provided algorithms are originally designed to solve monotone inclusion problems. After introducing basic notations and preliminary results in convex analysis, we derive two numerical methods based on different smoothing strategies for solving nondifferentiable convex optimization problems. The first approa

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20

Soheili, Azad Negar. "Elementary Algorithms for Solving Convex Optimization Problems." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/353.

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The rapid growth in data availability has led to modern large scale convex optimization problems that pose new practical and theoretical challenges. Examples include classification problems such as customer segmentation in retail and credit scoring in insurance. Classical optimization and machine learning techniques are typically inadequate to solve these large optimization problems because of high memory requirements and slow convergence guarantees. This thesis develops two research threads to address these issues. The first involves improving the effectiveness of a class of algorithms with s

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21

Grigas, Paul (Paul Edward). "Methods for convex optimization and statistical learning." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106683.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 219-225).<br>We present several contributions at the interface of first-order methods for convex optimization and problems in statistical machine learning. In the first part of this thesis, we present new results for the Frank-W

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22

Altschuler, Jason (Jason M. ). "Greed, hedging, and acceleration in convex optimization." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120409.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 153-156).<br>This thesis revisits the well-studied and practically motivated problem of minimizing a strongly convex, smooth function with first-order information. The first main message of the thesis is that, surprisingly, algorithms which are individually suboptimal can be combined to achieve accelerated convergence rates. This phenomenon can be intuively understood as "hedging" between saf

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23

Lee, Yin Tat. "Faster algorithms for convex and combinatorial optimization." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104467.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 443-458).<br>In this thesis, we revisit three algorithmic techniques: sparsification, cutting and collapsing. We use them to obtain the following results on convex and combinatorial optimization: --Linear Programming: We obtain the first improvement to the

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24

Gupta, Swati Ph D. Massachusetts Institute of Technology. "Combinatorial structures in online and convex optimization." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112014.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 157-163).<br>Motivated by bottlenecks in algorithms across online and convex optimization, we consider three fundamental questions over combinatorial polytopes. First, we study the minimization of separable strictly convex funct

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25

Li, Xinxin. "Some operator splitting methods for convex optimization." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/43.

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Many applications arising in various areas can be well modeled as convex optimization models with separable objective functions and linear coupling constraints. Such areas include signal processing, image processing, statistical learning, wireless networks, etc. If these well-structured convex models are treated as generic models and their separable structures are ignored in algorithmic design, then it is hard to eﬀectively exploit the favorable properties that the objective functions possibly have. Therefore, some operator splitting methods have regained much attention from diﬀerent areas for

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26

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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27

Kůdela, Jakub. "Advanced Decomposition Methods in Stochastic Convex Optimization." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-403864.

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Při práci s úlohami stochastického programování se často setkáváme s optimalizačními problémy, které jsou příliš rozsáhlé na to, aby byly zpracovány pomocí rutinních metod matematického programování. Nicméně, v některých případech mají tyto problémy vhodnou strukturu, umožňující použití specializovaných dekompozičních metod, které lze použít při řešení rozsáhlých optimalizačních problémů. Tato práce se zabývá dvěma třídami úloh stochastického programování, které mají speciální strukturu, a to dvoustupňovými stochastickými úlohami a úlohami s pravděpodobnostním omezením, a pokročilými dekompozi

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28

Merckx, Keno. "Optimization and Realizability Problems for Convex Geometries." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/288673/4/TOC.pdf.

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Convex geometries are combinatorial structures; they capture in an abstract way the essential features of convexity in Euclidean space, graphs or posets for instance. A convex geometry consists of a finite ground set plus a collection of subsets, called the convex sets and satisfying certain axioms. In this work, we study two natural problems on convex geometries. First, we consider the maximum-weight convex set problem. After proving a hardness result for the problem, we study a special family of convex geometries built on split graphs. We show that the convex sets of such a convex geometry r

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29

Zhang, Hongyi Ph D. Massachusetts Institute of Technology. "Topics in non-convex optimization and learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121830.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 165-186).<br>Non-convex optimization and learning play an important role in data science and machine learning, yet so far they still elude our understanding in many aspects. In this thesis, I study two important aspects of non-convex optimization and learning: Riemannian optimization and deep neural networks. In the first part, I develop iteration complexity analysis for Riemannian optimization, i.e., opti

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30

Chen, Jieqiu. "Convex relaxations in nonconvex and applied optimization." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/654.

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Traditionally, linear programming (LP) has been used to construct convex relaxations in the context of branch and bound for determining global optimal solutions to nonconvex optimization problems. As second-order cone programming (SOCP) and semidefinite programming (SDP) become better understood by optimization researchers, they become alternative choices for obtaining convex relaxations and producing bounds on the optimal values. In this thesis, we study the use of these convex optimization tools in constructing strong relaxations for several nonconvex problems, including 0-1 integer programm

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31

Rätsch, Gunnar. "Robust boosting via convex optimization theory and applications /." [S.l.] : [s.n.], 2001. http://pub.ub.uni-potsdam.de/2002/0008/raetsch.ps.

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Liberti, Leo Sergio. "Reformulation and convex relaxation techniques for global optimization." Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/11807.

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Lin, Tim Tai-Yi. "Primary estimation with sparsity-promoting bi-convex optimization." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/55900.

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This thesis establishes a novel inversion methodology for the surface-related primaries from a given recorded seismic wavefield, called the Robust Estimation of Primaries by Sparse Inversion (Robust EPSI, or REPSI). Surface-related multiples are a major source of coherent noise in seismic data, and inferring fine geological structures from active-source seismic recordings typically first necessitates its removal or mitigation. For this task, current practice calls for data-driven approaches which produce only approximate multiple models that must be non-linearly subtracted from the data, often

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34

Noel, Adam Josiah Gerald. "Convex sensing-reporting optimization for cooperative spectrum sensing." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/36838.

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In this thesis, we consider the cooperative spectrum sensing problem in cognitive radio with energy detection. Secondary users with non-identical, independent sensing channels make 1-bit sensing decisions and report their decisions to the secondary base station over orthogonal noisy fading channels. The base station has knowledge of the reporting channel coefficients and acts as a fusion center by combining the decisions with an M-out-of-K rule. We allow the secondary users to trade sensing time slots for additional reporting time slots to increase the signal-to-noise ratios of the reporting c

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35

Herr, Katrin [Verfasser]. "Core Sets and Symmetric Convex Optimization / Katrin Herr." München : Verlag Dr. Hut, 2013. http://d-nb.info/1045125504/34.

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Chandrasekaran, Venkat. "Convex optimization methods for graphs and statistical modeling." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66002.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 209-220).<br>An outstanding challenge in many problems throughout science and engineering is to succinctly characterize the relationships among a large number of interacting entities. Models based on graphs form one major thrust in this thesis, as graphs often provide a concise representation of the interactions among a large set of variables. A second major emphasis of this thesis are classes of

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Lintereur, Beau V. (Beau Vincent) 1973. "Constrained H̳₂ design via convex optimization with applications." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50628.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1998.<br>In title on t.p., double-underscored "H" appears in script.<br>Includes bibliographical references (p. 133-138).<br>A convex optimization controller design method is presented which minimizes the closed-loop H2 norm, subject to constraints on the magnitude of closed-loop transfer functions and transient responses due to specified inputs. This method uses direct parameter optimization of the closed-loop Youla or Q-parameter where the variables are the coefficients of a stable orthogonal basis.

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Anderson, James David. "Dynamical system decomposition and analysis using convex optimization." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:624001be-28d5-4837-a7d8-2222e270e658.

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This thesis is concerned with investigating new methods for the analysis of large-scale dynamical systems using convex optimization. The proposed methodology is based on composite Lyapunov theory and is computationally implemented using polynomial programming techniques. The main result of this work is the development of a system decomposition framework that makes it possible to analyze systems that are of such a scale that traditional methods cannot cope with. We begin by addressing the problem of model invalidation. A barrier certificate method for invalidating models in the presence of unce

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Wytock, Matt. "Optimizing Optimization: Scalable Convex Programming with Proximal Operators." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/785.

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Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex solvers have not yet reached sufficient maturity to fully decouple the convex programming model from the numerical algorithms required for implementation. Especially as datasets grow in size, there is a significant gap in speed and scalability between general solvers and specialized algorithms. This thesis addresses this gap with a new model for convex programming based on an intermediate representation of convex pr

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Goulet, Vincent. "Four-bar linkage synthesis using non-convex optimization." Master's thesis, Université Laval, 2017. http://hdl.handle.net/20.500.11794/27721.

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Ce mémoire présente une méthode pour synthétiser automatiquement des mécanismes articulés à quatre barres. Un logiciel implémentant cette méthode a été développé dans le cadre d’une initiative d’Autodesk Research portant sur la conception générative. Le logiciel prend une trajectoire en entrée et calcule les paramètres d’un mécanisme articulé à quatre barres capable de reproduire la même trajectoire. Ce problème de génération de trajectoire est résolu par optimisation non-convexe. Le problème est modélisé avec des contraintes quadratiques et des variables réelles. Une contrainte redondante spé

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Lin, Beldon Chi. "Integrated vehicle and mission design using convex optimization." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127070.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 177-183).<br>Convex optimization is used to solve the simultaneous vehicle and mission design problem. The objective of this work is to develop convex optimization architectures that allow both the vehicle and mission to be designed together. They allow the problem to be solved very quickly while maintaining similar fidelity to comparable methods. Multiple architectures are formulated, and the arch

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Sano, Yoshio. "Matroids on convex geometries: Subclasses, operations, and optimization." 京都大学 (Kyoto University), 2010. http://hdl.handle.net/2433/120626.

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Li, Jueyou. "Distributed and parallel methods for structural convex optimization." Thesis, Federation University Australia, 2014. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/81614.

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There has been considerable recent interest in optimization methods associated with a multi-agent network. The goal is to optimize a global objective function which is a sum of local objective functions only known by the agents through the network. The focus of this dissertation is the development of optimization algorithms for the special class when the optimization problem of interest has an additive or separable structure. Specifically, we are concerned with two classes of convex optimization problems. The first one is called as multi-agent convex problems and they arise in many network app

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Bengtsson, Gabriel, and Jesper Larsson. "Source Localization by Inverse Diffusion and Convex Optimization." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230738.

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Målet med rapporten har varit att att lokalisera källor till en emitterad substans med hjälp av en algoritm för inversdiffusion. Denna algoritm har gett lyckade resultat när den applicerats för biologisk detektering av celler och det har vidare föreslagits att körtiden skulle kunna reduceras avsevärt om algoritmen implementeras som en beräkningsgraf. Detta skulle automatisera beräkningar av gradienter och tillåta snabbare exekvering på en grafikprocessor. För algoritmens implementation användes TensorFlow, som primärt är ett programmeringsbibliotek inriktat mot maskininlärning. Datorgenererade

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Nguyen, Thanh Tan. "Selected non-convex optimization problems in machine learning." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200748/1/Thanh_Nguyen_Thesis.pdf.

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Non-convex optimization is an important and rapidly growing research area. It is tied to the latest success of deep learning, reinforcement learning, matrix factorization, and more. As a contribution to this area, this thesis provides analyses and algorithms for three important problems. The first one is optimization of noisy functions defined on a large graph, which is useful for AB testing, digital marketing. The second one is learning a convex ensemble of basis models, with application in regression and classification. The last one is optimization of ResNet with restricted residual modules,

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Cho, Myung. "Convex and non-convex optimizations for recovering structured data: algorithms and analysis." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5922.

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Optimization theories and algorithms are used to efficiently find optimal solutions under constraints. In the era of “Big Data”, the amount of data is skyrocketing,and this overwhelms conventional techniques used to solve large scale and distributed optimization problems. By taking advantage of structural information in data representations, this thesis offers convex and non-convex optimization solutions to various large scale optimization problems such as super-resolution, sparse signal processing,hypothesis testing, machine learning, and treatment planning for brachytherapy. Super-resolution

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Yang, Yi. "Sequential convex approximations of chance constrained programming /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?IELM%202008%20YANG.

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Banerjee, Nirjhar. "Random obtuse triangles and convex quadrilaterals." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54212.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 83-85).<br>We intend to discuss in detail two well known geometrical probability problems. The first one deals with finding the probability that a random triangle is obtuse in nature. We initially discuss the various ways of choosing a ran

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Umenberger, Jack. "Convex Identifcation of Stable Dynamical Systems." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17321.

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This thesis concerns the scalable application of convex optimization to data-driven modeling of dynamical systems, termed system identi cation in the control community. Two problems commonly arising in system identi cation are model instability (e.g. unreliability of long-term, open-loop predictions), and nonconvexity of quality-of- t criteria, such as simulation error (a.k.a. output error). To address these problems, this thesis presents convex parametrizations of stable dynamical systems, convex quality-of- t criteria, and e cient algorithms to optimize the latter over the former. In particu

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Andramonov, Mikhail. "Global minimization of some classes of generalized convex functions." Thesis, Federation University Australia, 2001. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164850.

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