[pt] Apresenta-se nesta dissertação um novo algoritmo para o cálculo de algumas funções distância entre polígonos convexos, no caso geral em que os polígonos podem se interseptar, cuja complexidade linear de pior caso é melhor do que a dos algoritmos até então conhecidos na literatura. O algoritmo é baseado em um algoritmo de complexidade linear originalmente proposto para determinação da distância de Hausdorff entre polígonos convexos disjuntos e utiliza como sua principal componente um algoritmo linear para o cálculo da interseção entre polígonos convexos. A motivação para o estudo de algoritmos eficientes para este problema de cálculo de distâncias decorre de aplicações em reconhecimento de formas e superposição ótima de contornos. Resultados computacionais também são apresentados. / [en] We present in this dissertation a new algorithm for the computation of some distance functions between convex polygons, in the general case where they can intersect, whose worst case time complexity is better than of the previously known algorithms. The algorthm is based on an algorithm originally proposed for the computation of the Hausdorff distance between disjoint polygons and uses as its main component a linear time algorithm for finding the intersection of convex polygons. The motivation for the study of efficient algorithms for this distance computation problem comes from applications in pattern recognition and contour fitting. Computatioal results are also presented.

Intersections tests between meshes in physics engines are time consuming and computationalheavy tasks. In order to speed up these intersection tests, each mesh can be decomposedinto several smaller convex hulls where the intersection test between each pair of these smallerhulls becomes more computationally efficient. The decomposition of meshes within the game industry is today performed by digital artistsand is considered a boring and time consuming task. Hence, the focus of this master thesislies in automatically decompose a mesh into several smaller convex hulls and to approximatethese decomposed pieces with bounding volumes of different complexity. These boundingvolumes together represents a collision mesh that is fully usable in modern games.

In this work, we focus on two kinds of problems involving the proximity of geometric objects. The first part revolves around intersection detection problems. In this setting, we are given two (or more) geometric objects and we are allowed to preprocess them. Then, the objects are translated and rotated within a geometric space, and we need to efficiently test if they intersect in these new positions. We develop representations of convex polytopes in any (constant) dimension that allow us to perform this intersection test in logarithmic time.In the second part of this work, we turn our attention to facility location problems. In this setting, we are given a set of sites in a geometric space and we want to place a facility at a specific place in such a way that the distance between the facility and its farthest site is minimized. We study first the constrained version of the problem, in which the facility can only be place within a given geometric domain. We then study the facility location problem under the geodesic metric. In this setting, we consider a different way to measure distances: Given a simple polygon, we say that the distance between two points is the length of the shortest path that connects them while staying within the given polygon. In both cases, we present algorithms to find the optimal location of the facility.In the process of solving facility location problems, we rely heavily on geometric structures called Voronoi diagrams. These structures summarize the proximity information of a set of ``simple'' geometric objects in the plane and encode it as a decomposition of the plane into interior disjoint regions whose boundaries define a plane graph. We study the problem of constructing Voronoi diagrams incrementally by analyzing the number of edge insertions and deletions needed to maintain its combinatorial structure as new sites are added. / Option Informatique du Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Dans cette thèse, nous donnons de nouveaux résultats sur la taille moyenne d’enveloppes convexes de points choisis dans un convexe. Cette taille est connue lorsque les points sont choisis uniformément (et indépendamment) dans un polytope convexe, ou un convexe suffisamment «lisse» ; ou encore lorsque les points sont choisis indépendamment selon une loi normale centrée. Dans la première partie de cette thèse, nous développons une technique nous permettant de donner de nouveaux résultats lorsque les points sont choisis arbitrairement dans un convexe, puis «bruités» par une perturbation aléatoire. Ce type d’analyse, appelée analyse lissée, a initialement été développée par Spielman et Teng dans leur étude de l’algorithme du simplexe. Pour un ensemble de points arbitraires dans une boule, nous obtenons une borne inférieure et une borne supérieure de cette complexité lissée dans le cas de perturbations uniformes dans une boule en dimension arbitraire, ainsi que dans le cas de perturbations gaussiennes en dimension 2. La taille de l'enveloppe convexe de points choisis uniformément dans un convexe, peut avoir un comportement logarithmique si ce convexe est un polytope ou polynomial s’il est lisse. Nous construisons un convexe produisant un comportement oscillant entre ces deux extrêmes. Dans la dernière partie, nous présentons un algorithme pour engendrer efficacement une enveloppe convexe aléatoire de points choisis uniformément et indépendamment dans un disque sans avoir à engendrer explicitement tous les points. Il a été implémenté en C++ et intégré dans la bibliothèque CGAL. / In this thesis, we give some new results about the average size of convex hulls made of points chosen in a convex body. This size is known when the points are chosen uniformly (and independently) in a convex polytope or in a "smooth" enough convex body. This average size is also known if the points are independently chosen according to a centered Gaussian distribution. In the first part of this thesis, we introduce a technique that will give new results when the points are chosen arbitrarily in a convex body, and then noised by some random perturbations. This kind of analysis, called smoothed analysis, has been initially developed by Spielman and Teng in their study of the simplex algorithm. For an arbitrary set of point in a ball, we obtain a lower and a upper bound for this smoothed complexity, in the case of uniform perturbation in a ball (in arbitrary dimension) and in the case of Gaussian perturbations in dimension 2. The asymptotic behavior of the expected size of the convex hull of uniformly random points in a convex body is polynomial for a "smooth" body and polylogarithmic for a polytope. In the second part, we construct a convex body so that the expected size of the convex hull of points uniformly chosen in that body oscillates between these two behaviors when the number of points increases. In the last part, we present an algorithm to generate efficiently a random convex hull made of points chosen uniformly and independently in a disk. We also compute its average time and space complexity. This algorithm can generate a random convex hull without explicitly generating all the points. It has been implemented in C++ and integrated in the CGAL library.

The convex hull of a sample is used to approximate the support of the underlying distribution. This approximation has many practical implications in real life. For example, approximating the boundary of a finite set is used by many authors in environmental studies and medical research. To approximate the functionals of convex hulls, asymptotic theory plays a crucial role. Unfortunately, the asymptotic results are mostly very complicated. To address this complication, we suggest a consistent bootstrapping scheme for certain cases. Our resampling technique is used for both semi-parametric and non-parametric cases. Let X1,X2,...,Xn be a sequence of i.i.d. random points uniformly distributed on an unknown convex set. Our bootstrapping scheme relies on resampling uniformly from the convex hull of X1,X2,...,Xn. In this thesis, we study the asymptotic consistency of certain functionals of convex hulls. In particular, we apply our bootstrapping technique to the Hausdorff distance between the actual convex set and its estimator. We also provide a conjecture for the application of our bootstrapping scheme to Gaussian polytopes. Moreover, some other relevant consistency results for the regular bootstrap are developed.

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A large portion of research in science and engineering, as well as in business, concerns one similar problem: how to make things "better”? Once properly modeled (although usually a highly nontrivial task), this kind of questions can be approached via a mathematical optimization problem. Optimal solution to a mathematical optimization problem, when interpreted properly, might corresponds to new knowledge, effective methodology or good decisions in corresponding application area. As already proved in many success stories, research in mathematical optimization has a significant impact on numerous aspects of human life. Recently, it was discovered that a large amount of difficult optimization problems can be formulated as copositive programming problems. Famous examples include a large class of quadratic optimization problems as well as many classical combinatorial optimization problems. For some more general optimization problems, copositive programming provides a way to construct tight convex relaxations. Because of this generality, new knowledge of copositive programs has the potential of being uniformly applied to these cases. While it is provably difficult to design efficient algorithms for general copositive programs, we study copositive programming from two standard aspects, its relaxations and its separation problem. With regard to constructing computational tractable convex relaxations for copositive programs, we develop direct constructions of two tensor relaxation hierarchies for the completely positive cone, which is a fundamental geometric object in copositive programming. We show connection of our relaxation hierarchies with known hierarchies. Then we consider the application of these tensor relaxations to the maximum stable set problem. With regard to the separation problem for copositive programming. We first prove some new results in low dimension of 5 x 5 matrices. Then we show how a separation procedure for this low dimensional case can be extended to any symmetric matrices with a certain block structure. Last but not least, we provide another approach to the separation and relaxations for the (generalized) completely positive cone. We prove some generic results, and discuss applications to the completely positive case and another case related to box-constrained quadratic programming. Finally, we conclude the thesis with remarks on some interesting open questions in the field of copositive programming.

The aim of this thesis is to systematically study the statistical guarantee for two representative non-convex optimization problems arsing in the statistics community. The first one is the high-dimensional Gaussian mixture model, which is motivated by the estimation of multiple graphical models arising from heterogeneous observations. The second one is the low-rank tensor estimation model, which is motivated by high-dimensional interaction model. Both optimal statistical rates and numerical comparisons are studied in depth. In the first part of my thesis, we consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical models. This is achieved via a high dimensional version of Expectation Conditional Maximization (ECM) algorithm. A joint graphical lasso penalty is imposed on the conditional maximization step to extract both homogeneity and heterogeneity components across all clusters. Our algorithm is computationally efficient due to fast sparse learning routines and can be implemented without unsupervised learning knowledge. The superior performance of our method is demonstrated by extensive experiments and its application to a Glioblastoma cancer dataset reveals some new insights in understanding the Glioblastoma cancer. In theory, a non-asymptotic error bound is established for the output directly from our high dimensional ECM algorithm, and it consists of two quantities: statistical error (statistical accuracy) and optimization error (computational complexity). Such a result gives a theoretical guideline in terminating our ECM iterations. In the second part of my thesis, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is developed based on sparse tensor decomposition and thresholded gradient descent, which ensures exact recovery in the noiseless case and stable recovery in the noisy case with high probability. The non-asymptotic analysis sheds light on an interplay between optimization error and statistical error. The proposed procedure is shown to be rate-optimal under certain conditions. As a technical by-product, novel high-order concentration inequalities are derived for studying high-moment sub-Gaussian tensors. An interesting tensor formulation illustrates the potential application to high-order interaction pursuit in high-dimensional linear regression

The problems we studied in this thesis are linearly constrained convex programming (LCCP) and nonnegative matrix factorization (NMF). The resolutions of these two problems are all closely related to convex programming with simple convex constraints. The work can mainly be described in the following three parts. Firstly, an interior point algorithm following a parameterized central path for linearly constrained convex programming is proposed. The convergence and polynomial-time complexity are proved under the assumption that the Hessian of the objective function is locally Lipschitz continuous. Also, an initialization strategy is proposed, and some numerical results are provided to show the efficiency of the proposed algorithm. Secondly, the path following algorithm is promoted for general barrier functions. A class of barrier functions is proposed, and their corresponding paths are proved to be continuous and converge to optimal solutions. Applying the path following algorithm to these paths provide more flexibility to interior point methods. With some adjustments, the initialization method is equipped to validate implementation and convergence. Thirdly, we study the convergence of hierarchical alternating least squares algorithm (HALS) and its fast form (Fast HALS) for nonnegative matrix factorization. The coordinate descend idea for these algorithms is restated. With a precise estimation of objective reduction, some limiting properties are illustrated. The accumulation points are proved to be stationary points, and some adjustments are proposed to improve the implementation and efficiency

Cognitive radio (CR) is a cutting-edge wireless communication technology that adopts several existing communication concepts in order to efficiently utilize the spectrum and meet the users demands of high throughput and real-time systems. Conventionally, high throughput demands are met through adopting broadband and multi-antenna technologies such as, orthogonal frequency division multiplexing (OFDM) and Multi-Input Multi-Output (MIMO). Whereas, real-time application demands are met by analyzing metrics which characterize the delay limited channels, such as, outage probability over block-fading channels. Being an environmental friendly technology, energy efficiency metrics should be considered in the design of a CR application. This thesis tackles the energy efficiency of CR system from different aspects, utilizing different measuring metrics and constrains. Under the single-input single-output (SISO) OFDM we minimized the energy per goodbit (EPG) metric subject to several power and Quality of Service (QoS) constraints. In this approach, the minimum EPG metric is optimized via proposing two optimal and sub-optimal resource allocation schemes. We consider several parameters as optimization variables, such as, power policy, sensing threshold, and channel quality threshold. We also captured the impact of involving the media access control (MAC) layers parameters, such as, frame length, in the minimization of a modified EPG metric. Also, a MAC protocol, i.e., hybrid automatic repeat request (HARQ), and the associated power consumption of the retransmission mechanism is considered in the formulation of the problem. In this context, the optimal power and frame length are derived to minimize the modified EPG while considering several spectrum-sharing scenarios, which depend on sensing information. In MIMO based CR system, we maximized capacity to power ratio (CPR) (as an energy efficiency (EE) metric) subject to several power and QoS constraints. In this context, the impact of sensing information with imperfect channel state information (CSI) of the secondary channel has been considered. To realize a CR system with real-time applications we minimized the outage probability over M block-fading channel with several long-term and short-term energy constrains. We derive the minimum outage region and the associated optimal power. Tractable expressions to lower and upper bound the outage probability are derived. We then analyze the impact of utilizing the sensing process of primary user activity.