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Structured sparsity-inducing norms : statistical and algorithmic properties with applications to neuroimaging / Normes parcimonieuses structurées : propriétés statistiques et algorithmiques avec applications à l’imagerie cérébraleJenatton, Rodolphe 24 November 2011 (has links)
De nombreux domaines issus de l’industrie et des sciences appliquées ont été les témoins d’une révolution numérique. Cette dernière s’est accompagnée d’une croissance du volume des données, dont le traitement est devenu un défi technique. Dans ce contexte, la parcimonie est apparue comme un concept central en apprentissage statistique. Il est en effet naturel de vouloir exploiter les données disponibles via un nombre réduit de paramètres. Cette thèse se concentre sur une forme particulière et plus récente de parcimonie, nommée parcimonie structurée. Comme son nom l’indique, nous considérerons des situations où, au delà de la seule parcimonie, nous aurons également à disposition des connaissances a priori relatives à des propriétés structurelles du problème. L’objectif de cette thèse est d'analyser le concept de parcimonie structurée, en se basant sur des considérations statistiques, algorithmiques et appliquées. Nous commencerons par introduire une famille de normes structurées parcimonieuses dont les aspects statistiques sont étudiées en détail. Nous considérerons ensuite l’apprentissage de dictionnaires, où nous exploiterons les normes introduites précédemment dans un cadre de factorisation de matrices. Différents outils algorithmiques efficaces, tels que des méthodes proximales, seront alors proposés. Grâce à ces outils, nous illustrerons sur de nombreuses applications pourquoi la parcimonie structurée peut être bénéfique. Ces exemples contiennent des tâches de restauration en traitement de l’image, la modélisation hiérarchique de documents textuels, ou encore la prédiction de la taille d’objets à partir de signaux d’imagerie par résonance magnétique fonctionnelle. / Numerous fields of applied sciences and industries have been recently witnessing a process of digitisation. This trend has come with an increase in the amount digital data whose processing becomes a challenging task. In this context, parsimony, also known as sparsity, has emerged as a key concept in machine learning and signal processing. It is indeed appealing to exploit data only via a reduced number of parameters. This thesis focuses on a particular and more recent form of sparsity, referred to as structured sparsity. As its name indicates, we shall consider situations where we are not only interested in sparsity, but where some structural prior knowledge is also available. The goal of this thesis is to analyze the concept of structured sparsity, based on statistical, algorithmic and applied considerations. To begin with, we introduce a family of structured sparsity-inducing norms whose statistical aspects are closely studied. In particular, we show what type of prior knowledge they correspond to. We then turn to sparse structured dictionary learning, where we use the previous norms within the framework of matrix factorization. From an optimization viewpoint, we derive several efficient and scalable algorithmic tools, such as working-set strategies and proximal-gradient techniques. With these methods in place, we illustrate on numerous real-world applications from various fields, when and why structured sparsity is useful. This includes, for instance, restoration tasks in image processing, the modelling of text documents as hierarchy of topics, the inter-subject prediction of sizes of objects from fMRI signals, and background-subtraction problems in computer vision.
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Familias normais de aplicações holomorfas em espaços de dimensão infinita / Normal families of holomorphic mappings on infinite dimensional spacesTakatsuka, Paula 15 February 2006 (has links)
Orientador: Jorge Tulio Mujica Ascui / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-07T16:44:31Z (GMT). No. of bitstreams: 1
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Previous issue date: 2006 / Resumo: Este trabalho estende teoremas clássicos da teoria de funções holomorfas de uma variável complexa para espaços localmente convexos de dimensão infinita. Serão dadas várias caracterizações de famílias normais, n¿ao apenas com relação à topologia compacto-aberta, mas também para outras topologias naturais no espaço de aplicações holomorfas. Teoremas de tipo Montel e de tipo Schottky, bem como outros resultados correlatos, ser¿ao estabelecidos em dimensão infinita para as diferentes topologias. Teoremas de limita¸c¿ao universal sobre famílias de funções holomorfas que omitem dois valores distintos ser¿ao formulados para espaços de Banach / Abstract: The present work extends some classical theorems from the theory of holomorphic functions of one complex variable to infinite dimensional locally convex spaces. Several characterizations of normal families are given, not only for the compact-open topology, but also for other natural topologies on spaces of holomorphic mappings. Montel-type and Schottky-type theorems and various related results are established in infinite dimension for these different topologies. Universal boundedness theorems concerning families of holomorphic functions which omit two distinct values are formulated for Banach spaces / Doutorado / Mestre em Matemática
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Sobre problemas associados a cones de segunda ordem / About problems related to second order conesDetsch, Denise Trevisoli, 1983- 18 August 2018 (has links)
Orientador: Maria Aparecida Diniz Ehrhardt / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-18T12:52:22Z (GMT). No. of bitstreams: 1
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Previous issue date: 2011 / Resumo: Este trabalho teve como foco o estudo de problemas SOCP, tanto nos seus aspectos teóricos quanto nos seus aspectos práticos. Problemas SOCP são problemas convexos de otimização nos quais uma função linear 'e minimizada sobre restrições lineares e restrições de cone quadrático. Tivemos dois objetivos principais: estudar o conceito, as aplicações e os métodos de resolução de problemas SOCP, permitindo verificar a viabilidade de trabalhar com tais problemas; e verificar na prática o benefício de se utilizar uma ferramenta específica de SOCP para a resolução de problemas que se enquadram nessa classe. Para a avaliação prática utilizamos um software de otimização genérica (fmincon) e outro específico de SOCP (CVXOPT). A análise ficou concentrada nos requisitos robustez, número de iterações e variação do tempo com o aumento da dimensão dos problemas. Diante dos resultados obtidos com os testes numéricos, pudemos concluir que 'e interessante usar SOCP sempre que possível / Abstract: This dissertation focuses on the study of SOCP problems, both in its theoretical, and in its practical aspects. SOCP problems are convex optimization problems in which a linear function is minimized over linear constraints and second-order cone constraints. We had two main objectives: study the concept, applications and methods for solving the SOCP problem, making it possible to verify the feasibility of working with such problems; and to verify the practical benefits of using a SOCP specific tool for the resolution of problems of this class. The experimental evaluation used a generic optimization software (fmincon) and other SOCP specific software (CVXOPT). The analysis was concentrated on the robustness, number of iterations and time variation with the increasing scale of the problems. From results obtained with the numerical tests, we concluded that SOCP is worth to be used whenever possible / Mestrado / Matematica Aplicada / Mestre em Matemática Aplicada
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Convex Cycle BasesHellmuth, Marc, Leydold, Josef, Stadler, Peter F. January 2014 (has links) (PDF)
Convex cycles play a role e.g. in the context of product graphs. We introduce convex cycle bases and describe a polynomial-time algorithm that recognizes whether a given graph has a convex cycle basis and provides an explicit construction in the positive case. Relations between convex cycles bases and other types of cycles bases are discussed. In particular we show that if G has a unique minimal cycle bases, this basis is convex. Furthermore, we characterize a class of graphs with convex cycles bases that includes partial cubes and hence median graphs.
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Sex-Specific Patterns of Movement and Space Use in the Strawberry Poison Frog, Oophaga pumilioMurasaki, Seiichi 28 June 2010 (has links)
The home range encompasses an animal’s movements as it goes about its normal activity, and several home range estimators have been developed. I evaluated the performance of the Minimum Convex Polygon, Bivariate Normal, and several kernel home range estimators in a geographical information system environment using simulations and a large database of O. pumilio mark-recapture locations. A fixed 90% kernel estimator using Least-Square Cross-Validation (to select the bandwidth) outperformed other methods of estimating home range size and was effective with relatively few capture points. Home range size, core area size, intrasexual overlap, and movement rates among coordinates were higher in female frogs than in male frogs. These measures likely reflect behavioral differences related to territoriality (males only) and parental care (both sexes). The simple Biological Index of Vagility (BIV) generated movement values that scaled well with home range size while revealing more information than home range estimates alone.
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Wavelet transform modulus : phase retrieval and scattering / Transformée en ondelettes : reconstruction de phase et de scatteringWaldspurger, Irène 10 November 2015 (has links)
Les tâches qui consistent à comprendre automatiquement le contenu d’un signal naturel, comme une image ou un son, sont en général difficiles. En effet, dans leur représentation naïve, les signaux sont des objets compliqués, appartenant à des espaces de grande dimension. Représentés différemment, ils peuvent en revanche être plus faciles à interpréter. Cette thèse s’intéresse à une représentation fréquemment utilisée dans ce genre de situations, notamment pour analyser des signaux audio : le module de la transformée en ondelettes. Pour mieux comprendre son comportement, nous considérons, d’un point de vue théorique et algorithmique, le problème inverse correspondant : la reconstruction d’un signal à partir du module de sa transformée en ondelettes. Ce problème appartient à une classe plus générale de problèmes inverses : les problèmes de reconstruction de phase. Dans un premier chapitre, nous décrivons un nouvel algorithme, PhaseCut, qui résout numériquement un problème de reconstruction de phase générique. Comme l’algorithme similaire PhaseLift, PhaseCut utilise une relaxation convexe, qui se trouve en l’occurence être de la même forme que les relaxations du problème abondamment étudié MaxCut. Nous comparons les performances de PhaseCut et PhaseLift, en termes de précision et de rapidité. Dans les deux chapitres suivants, nous étudions le cas particulier de la reconstruction de phase pour la transformée en ondelettes. Nous montrons que toute fonction sans fréquence négative est uniquement déterminée (à une phase globale près) par le module de sa transformée en ondelettes, mais que la reconstruction à partir du module n’est pas stable au bruit, pour une définition forte de la stabilité. On démontre en revanche une propriété de stabilité locale. Nous présentons également un nouvel algorithme de reconstruction de phase, non-convexe, qui est spécifique à la transformée en ondelettes, et étudions numériquement ses performances. Enfin, dans les deux derniers chapitres, nous étudions une représentation plus sophistiquée, construite à partir du module de transformée en ondelettes : la transformée de scattering. Notre but est de comprendre quelles propriétés d’un signal sont caractérisées par sa transformée de scattering. On commence par démontrer un théorème majorant l’énergie des coefficients de scattering d’un signal, à un ordre donné, en fonction de l’énergie du signal initial, convolé par un filtre passe-haut qui dépend de l’ordre. On étudie ensuite une généralisation de la transformée de scattering, qui s’applique à des processus stationnaires. On montre qu’en dimension finie, cette transformée généralisée préserve la norme. En dimension un, on montre également que les coefficients de scattering généralisés d’un processus caractérisent la queue de distribution du processus. / Automatically understanding the content of a natural signal, like a sound or an image, is in general a difficult task. In their naive representation, signals are indeed complicated objects, belonging to high-dimensional spaces. With a different representation, they can however be easier to interpret. This thesis considers a representation commonly used in these cases, in particular for theanalysis of audio signals: the modulus of the wavelet transform. To better understand the behaviour of this operator, we study, from a theoretical as well as algorithmic point of view, the corresponding inverse problem: the reconstruction of a signal from the modulus of its wavelet transform. This problem belongs to a wider class of inverse problems: phase retrieval problems. In a first chapter, we describe a new algorithm, PhaseCut, which numerically solves a generic phase retrieval problem. Like the similar algorithm PhaseLift, PhaseCut relies on a convex relaxation of the phase retrieval problem, which happens to be of the same form as relaxations of the widely studied problem MaxCut. We compare the performances of PhaseCut and PhaseLift, in terms of precision and complexity. In the next two chapters, we study the specific case of phase retrieval for the wavelet transform. We show that any function with no negative frequencies is uniquely determined (up to a global phase) by the modulus of its wavelet transform, but that the reconstruction from the modulus is not stable to noise, for a strong notion of stability. However, we prove a local stability property. We also present a new non-convex phase retrieval algorithm, which is specific to the case of the wavelet transform, and we numerically study its performances. Finally, in the last two chapters, we study a more sophisticated representation, built from the modulus of the wavelet transform: the scattering transform. Our goal is to understand which properties of a signal are characterized by its scattering transform. We first prove that the energy of scattering coefficients of a signal, at a given order, is upper bounded by the energy of the signal itself, convolved with a high-pass filter that depends on the order. We then study a generalization of the scattering transform, for stationary processes. We show that, in finite dimension, this generalized transform preserves the norm. In dimension one, we also show that the generalized scattering coefficients of a process characterize the tail of its distribution.
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STUDIES ON ALTERNATING DIRECTION METHOD OF MULTIPLIERS WITH ADAPTIVE PROXIMAL TERMS FOR CONVEX OPTIMIZATION PROBLEMS / 凸最適化問題に対する適応的な近接項付き交互方向乗数法に関する研究Gu, Yan 24 November 2020 (has links)
京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22862号 / 情博第741号 / 新制||情||127(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 山下 信雄, 教授 太田 快人, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM
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A Geometry-Based Multiple Testing Correction for Contingency Tables by Truncated Normal Distribution / 切断正規分布を用いた分割表の幾何学的マルチプルテスティング補正法Basak, Tapati 24 May 2021 (has links)
京都大学 / 新制・課程博士 / 博士(医学) / 甲第23367号 / 医博第4736号 / 新制||医||1051(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 森田 智視, 教授 川上 浩司, 教授 佐藤 俊哉 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM
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Provably efficient algorithms for decentralized optimizationLiu, Changxin 31 August 2021 (has links)
Decentralized multi-agent optimization has emerged as a powerful paradigm that finds broad applications in engineering design including federated machine learning and control of networked systems. In these setups, a group of agents are connected via a network with general topology. Under the communication constraint, they aim to solving a global optimization problem that is characterized collectively by their individual interests. Of particular importance are the computation and communication efficiency of decentralized optimization algorithms. Due to the heterogeneity of local objective functions, fostering cooperation across the agents over a possibly time-varying network is challenging yet necessary to achieve fast convergence to the global optimum. Furthermore, real-world communication networks are subject to congestion and bandwidth limit. To relieve the difficulty, it is highly desirable to design communication-efficient algorithms that proactively reduce the utilization of network resources. This dissertation tackles four concrete settings in decentralized optimization, and develops four provably efficient algorithms for solving them, respectively.
Chapter 1 presents an overview of decentralized optimization, where some preliminaries, problem settings, and the state-of-the-art algorithms are introduced. Chapter 2 introduces the notation and reviews some key concepts that are useful throughout this dissertation. In Chapter 3, we investigate the non-smooth cost-coupled decentralized optimization and a special instance, that is, the dual form of constraint-coupled decentralized optimization. We develop a decentralized subgradient method with double averaging that guarantees the last iterate convergence, which is crucial to solving decentralized dual Lagrangian problems with convergence rate guarantee. Chapter 4 studies the composite cost-coupled decentralized optimization in stochastic networks, for which existing algorithms do not guarantee linear convergence. We propose a new decentralized dual averaging (DDA) algorithm to solve this problem. Under a rather mild condition on stochastic networks, we show that the proposed DDA attains an $\mathcal{O}(1/t)$ rate of convergence in the general case and a global linear rate of convergence if each local objective function is strongly convex. Chapter 5 tackles the smooth cost-coupled decentralized constrained optimization problem. We leverage the extrapolation technique and the average consensus protocol to develop an accelerated DDA algorithm. The rate of convergence is proved to be $\mathcal{O}\left( \frac{1}{t^2}+ \frac{1}{t(1-\beta)^2} \right)$, where $\beta$ denotes the second largest singular value of the mixing matrix. To proactively reduce the utilization of network resources, a communication-efficient decentralized primal-dual algorithm is developed based on the event-triggered broadcasting strategy in Chapter 6. In this algorithm, each agent locally determines whether to generate network transmissions by comparing a pre-defined threshold with the deviation between the iterates at present and lastly broadcast. Provided that the threshold sequence is summable over time, we prove an $\mathcal{O}(1/t)$ rate of convergence for convex composite objectives. For strongly convex and smooth problems, linear convergence is guaranteed if the threshold sequence is diminishing geometrically. Finally, Chapter 7 provides some concluding remarks and research directions for future study. / Graduate
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Problematika pozorování objektů v dopravním zrcadle / Problem of Object Observation in Traffic MirrorAdamec, Martin January 2013 (has links)
The thesis is written up within the master degree in field Expert engineering in transport. It follows up Problem of Object Observation in Traffic Mirror. Thesis describes the device of traffic mirror, physical principal, legislation and types of traffic mirrors in detail. There are measurements associated with this issue in practical part, which are evaluated in detail. At the conclusion there are suggested changes and recommendations associated with this issue.
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