Este trabalho é focado num problema clássico das Ciências e Engenharia, que consiste em cobrir um objeto por esferas de mesmo raio, a ser minimizado. A abordagem prática usual conta com sérias desvantagens. Logo, faz-se necessário trabalhar com isto de forma diferenciada. A técnica proposta aqui envolve a utilização de resultados célebres da geometria algébrica real, que tem como peça central o positivstellensatz de Stengle e, fazendo a devida relação entre esses resultados e otimização com restrições envolvendo representações naturais por somas de quadrados, é possível reduzir o problema original a um de programação semidefinida não linear. Mas, por contar com particularidades que favorecem a aplicação do paradigma de restauração inexata, esta foi a técnica utilizada para resolvê-lo. A versatilidade da técnica e a possibilidade de generalização direta dos objetos envolvidos destacam-se como grandes vantagens desta abordagem, além da visão algébrica inovadora do problema. / This work is focused on a classic problem from Engineering. Basically, it consists of finding the optimal positioning and radius of a set of equal spheres in order to cover a given object. The common approach to this carries some substantial disadvantages, what makes it necessary to nd a dierent way. Here, we explore some renowned results from real algebraic geometry, which has Stengle\'s positivstellensatz as one of its central pieces, and SOS optimization. Once the proper link is made, the original problem can be reduced to a nonlinear semidenite programming one, which has peculiarities that favours the application of an inexact restoration paradigm. We point out the algebraic view and the no use of discretizations as great advantages of this approach, besides the notable versatility and easy generalization in terms of dimension and involved objects.

Este trabalho é focado num problema clássico das Ciências e Engenharia, que consiste em cobrir um objeto por esferas de mesmo raio, a ser minimizado. A abordagem prática usual conta com sérias desvantagens. Logo, faz-se necessário trabalhar com isto de forma diferenciada. A técnica proposta aqui envolve a utilização de resultados célebres da geometria algébrica real, que tem como peça central o positivstellensatz de Stengle e, fazendo a devida relação entre esses resultados e otimização com restrições envolvendo representações naturais por somas de quadrados, é possível reduzir o problema original a um de programação semidefinida não linear. Mas, por contar com particularidades que favorecem a aplicação do paradigma de restauração inexata, esta foi a técnica utilizada para resolvê-lo. A versatilidade da técnica e a possibilidade de generalização direta dos objetos envolvidos destacam-se como grandes vantagens desta abordagem, além da visão algébrica inovadora do problema. / This work is focused on a classic problem from Engineering. Basically, it consists of finding the optimal positioning and radius of a set of equal spheres in order to cover a given object. The common approach to this carries some substantial disadvantages, what makes it necessary to nd a dierent way. Here, we explore some renowned results from real algebraic geometry, which has Stengle\'s positivstellensatz as one of its central pieces, and SOS optimization. Once the proper link is made, the original problem can be reduced to a nonlinear semidenite programming one, which has peculiarities that favours the application of an inexact restoration paradigm. We point out the algebraic view and the no use of discretizations as great advantages of this approach, besides the notable versatility and easy generalization in terms of dimension and involved objects.

Cette dernière décennie, la modélisation des villes 3D est devenue l'un des enjeux de la recherche multimédia et un axe important en reconnaissance d'objets. Dans cette thèse nous nous sommes intéressés à localiser différentes primitives, plus particulièrement les fenêtres, dans les façades de Paris. Dans un premier temps, nous présentons une analyse des façades et des différentes propriétés des fenêtres. Nous en déduisons et proposons ensuite un algorithme capable d'extraire automatiquement des hypothèses de fenêtres. Dans une deuxième partie, nous abordons l'extraction et la reconnaissance des primitives à l'aide d'appariement de graphes de contours. En effet une image de contours est lisible par l'oeil humain qui effectue un groupement perceptuel et distingue les entités présentes dans la scène. C'est ce mécanisme que nous avons cherché à reproduire. L'image est représentée sous la forme d'un graphe d'adjacence de segments de contours, valué par des informations d'orientation et de proximité des segments de contours. Pour la mise en correspondance inexacte des graphes, nous proposons plusieurs variantes d'une nouvelle similarité basée sur des ensembles de chemins tracés sur les graphes, capables d'effectuer les groupements des contours et robustes aux changements d'échelle. La similarité entre chemins prend en compte la similarité des ensembles de segments de contours et la similarité des régions définies par ces chemins. La sélection des images d'une base contenant un objet particulier s'effectue à l'aide d'un classifieur SVM ou kppv. La localisation des objets dans l'image utilise un système de vote à partir des chemins sélectionnés par l'algorithme d'appariement.

Orientador: Marcia Aparecida Gomes Ruggiero / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-16T01:38:07Z (GMT). No. of bitstreams: 1 Reis_DiegoDerivaldodos_M.pdf: 1640617 bytes, checksum: 7bb1fcda0049b1ac1e6645b7ce8789eb (MD5) Previous issue date: 2010 / Resumo: Uma das estratégias empregadas para resolver vim problema de programação não linear com restrições é usar métodos iterativos que geram uma seqüência de pontos viáveis. A razão é que frequentemente soluções viáveis são úteis em aplicações da engenharia, física ou química, ao contrário das aproximações não viáveis, até mesmo quando estas estão bem próximas do valor ótimo. Porém, quando lidamos com restrições não lineares não suaves, é difícil manter viabilidade e, simultaneamente, melhorar o valor da função objetivo. Uma alternativa é empregar métodos de Restauração Inexata. Em linhas gerais, nestes métodos, a cada iteração dois novos pontos são gerados, um que visa melhorar a viabilidade e outro que diminui o valor da função objetivo. Um terceiro ponto é obtido de modo a atingir um decréscimo mínimo de uma função de mérito composta pelos dois primeiros pontos e que busca o equilíbrio entre viabilidade e otimalidade. Ao processo de encontrar o ponto que melhora a viabilidade, damos o nome de restauração e o objetivo central deste trabalho é analisar esta fase. Analisamos problemas de otimização onde as restrições são não lineares acrescidas por restrições de canalização (limitantes inferior e superior para as variáveis). Para realizar a restauração usamos o método proposto por J. B. Francisco, N. Krejic e J. M. Martinez [11], no qual são considerados sistemas não lineares com restrições de canalização e que faz uso de uma estratégia de região dc confiança com escalamento. O método de Restauração Inexata que usamos é baseado no algoritmo proposto por M. A. Gomes Ruggiero, J. M. Martinez e S. A. Santos [13] que emprega a direção do gradiente espectral projetado [4] para resolver o problema, de hard-spheres. onde a restauração pode ser sempre feita de maneira exata. Neste trabalho resolvemos problemas nos quais a fase de restauração não é necessariamente feita de maneira exata. Os testes computacionais, realizados com problemas acadêmicos, atestam a eficiência do esquema proposto. Usando o algoritmo proposto em [11] para realizar a fase de restauração, implementamos no software MatLab 7.7 o algoritmo do método de Restauração Inexata, encontrado em [13], utilizando o mesmo conjunto de problemas teste usados em [11] além de outros encontrados em [14], obtendo bons resultados / Abstract: One of the strategies employed to solve a nonlinear programming problem with constraints is to use iterative methods that generate a sequence of points feasible. The reason is that viable solutions are often useful in applications engineering, physics or chemistry, unlike the approaches are not viable, even when they are very close to the optimum value. But when dealing with soft constraints nonlinear, it is difficult to maintain viability and, simultaneously, improve the value of the objective function. An alternative is to employ methods of Inexact Restoration. In general, these methods, each iteration two new points are generated, one that aims to improve the viability and another that decreases the value of the objective function. A third point is obtained in order to achieve a decrease of at least a merit function consisting of the first two points and that seeks a balance between feasibility and optimality. The process of finding the point that improves the viability, we give the name of restoration and purpose of this paper is to analyze this phase. We analyze optimization problems where the constraints are nonlinear constraints added by channeling (lower and upper bounds for variables). To accomplish the restoration we use the method proposed by Mr B. Francis, N. Kreji'c and J. M. Martinez [11], which are considered non-linear systems with restricted channel that uses a trust region strategy with scaling. The Inexact restoration method we use is based on the algorithm proposed by M. A. Gomes Ruggiero, J. M. Martinez and S. A. Santos [13] that employs the spectral projected gradient direction [4] to solve the problem of hard-spheres, where the restoration can be done in exactly. Present paper, problems in which phase of restoration is not necessarily done exactly. The computational tests carried out with academic problems, proving the efficiency of the proposed scheme. Using the algorithm proposed in [11] to accomplish the restoration phase, implemented in MatLab 7.7 the algorithm of the method of Inexact Restoration, found in [13], using the same set of test problems used in [11] and other found in [14], obtaining good results / Mestrado / Otimização / Mestre em Matemática Aplicada

Accurate forecasts of weather and climate will become increasingly important as the world adapts to anthropogenic climatic change. Forecasts' accuracy is limited by the computer power available to forecast centres, which determines the maximum resolution, ensemble size and complexity of atmospheric models. Furthermore, faster supercomputers are increasingly energy-hungry and unaffordable to run. In this thesis, a new means of making computer simulations more efficient is presented that could lead to more accurate forecasts without increasing computational costs. This 'scale-selective reduced precision' technique builds on previous work that shows that weather models can be run with almost all real numbers represented in 32 bit precision or lower without any impact on forecast accuracy, challenging the paradigm that 64 bits of numerical precision are necessary for sufficiently accurate computations. The observational and model errors inherent in weather and climate simulations, combined with the sensitive dependence on initial conditions of the atmosphere and atmospheric models, renders such high precision unnecessary, especially at small scales. The 'scale-selective' technique introduced here therefore represents smaller, less influential scales of motion with less precision. Experiments are described in which reduced precision is emulated on conventional hardware and applied to three models of increasing complexity. In a three-scale extension of the Lorenz '96 toy model, it is demonstrated that high resolution scale-dependent precision forecasts are more accurate than low resolution high-precision forecasts of a similar computational cost. A spectral model based on the Surface Quasi-Geostrophic Equations is used to determine a power law describing how low precision can be safely reduced as a function of spatial scale; and experiments using four historical test-cases in an open-source version of the real-world Integrated Forecasting System demonstrate that a similar power law holds for the spectral part of this model. It is concluded that the scale-selective approach could be beneficially employed to optimally balance forecast cost and accuracy if utilised on real reduced precision hardware.

Consider an under-determined system of nonlinear equations F(x)=0, F:R^m→R^n, where F is continuously differentiable and m > n. This system appears in a variety of applications, including parameter-dependent systems, dynamical systems with periodic solutions, and nonlinear eigenvalue problems. Robust, efficient numerical methods are often required for the solution of this system. Newton's method is an iterative scheme for solving the nonlinear system of equations F(x)=0, F:R^n→R^n. Simple to implement and theoretically sound, it is not, however, often practical in its pure form. Inexact Newton methods and globalized inexact Newton methods are computationally efficient variations of Newton's method commonly used on large-scale problems. Frequently, these variations are more robust than Newton's method. Trust region methods, thought of here as globalized exact Newton methods, are not as computationally efficient in the large-scale case, yet notably more robust than Newton's method in practice. The normal flow method is a generalization of Newton's method for solving the system F:R^m→R^n, m > n. Easy to implement, this method has a simple and useful local convergence theory; however, in its pure form, it is not well suited for solving large-scale problems. This dissertation presents new methods that improve the efficiency and robustness of the normal flow method in the large-scale case. These are developed in direct analogy with inexact-Newton, globalized inexact-Newton, and trust-region methods, with particular consideration of the associated convergence theory. Included are selected problems of interest simulated in MATLAB.

L'isomorphisme inexact de graphes est un problème crucial pour la définition d'une distance entre graphes, préalable nécessaire à une multitude d'applications allant de l'analyse d'images à des applications biomédicales en passant par la reconnaissance optique de caractères. Ce problème est encore plus complexe que celui de l'isomorphisme exact. Alors que ce dernier est un problème de décision de complexité au moins de classe P et qui ne s'applique qu'à des graphes exactement identiques, l'isomorphisme inexact est un problème combinatoire de complexité de classe NP qui permet de prendre en compte des perturbations dues au bruit, qui apparaissent fréquemment dans les applications réelles. Dans ce cadre, nous choisissons d'étudier une solution basée sur les algorithmes génétiques pouvant être appliquée à l'isomorphisme exact et inexact. Nous proposons des opérateurs de croisement généraux pour tout problème représenté par un codage de permutation, ainsi que des opérateurs spécifiques à l'isomorphisme de graphes qui exploitent une heuristique gloutonne. Nous réalisons une étude exhaustive pour comparer ces opérateurs avec les opérateurs existants, soulignant leurs propriétés, avantages et inconvénients respectifs. Nous étudions par ailleurs plusieurs pistes d'amélioration de l'algorithme, en théorie ou en pratique, considérant successivement les objectifs d'accélération de l'exécution, d'augmentation de la précision et de garantie de résultat optimal. Nous proposons pour cela de combiner l'approche proposée avec d'autres techniques telles que des heuristiques générales comme la recherche locale, des heuristiques dédiées comme l'algorithme A*, et des outils pratiques comme la parallélisation. Ces travaux conduisent à la définition d'une méthode générique pour la résolution de tous les problèmes d'isomorphismes de graphes, qu'il s'agisse d'isomorphismes exact ou inexact, d'isomorphismes de graphes de même taille ou d'isomorphismes de sous-graphes. Nous illustrons enfin la validité de cette solution générale par trois applications concrètes issues de domaines différents, la recherche d'images et la chimie, qui présentent chacune des caractéristiques spécifiques, utilisant des graphes attribués ou non, soumis aux perturbations plutôt structurelles ou au niveau d'attributs.

The development, analysis, and implementation of efficient methods to solve algebraic systems of equations are main research directions in the field of numerical simulation and are the focus of this thesis. Due to their lesser demands for computer resources, iterative solution methods are the choice to make, when very large scale simulations have to be performed. To improve their efficiency, iterative methods are combined with proper techniques to accelerate convergence. A general technique to do this is to use a so-called preconditioner. Constructing and analysing various preconditioning methods has been an active field of research already for decades. Special attention is devoted to the class of the so-called optimal order preconditioners, that possess both optimal convergence rate and optimal computational complexity. The preconditioning techniques, proposed and studied in this thesis, utilise the block structure of the underlying matrices, and lead to methods that are of optimal order. In the first part of the thesis, we construct an Algebraic MultiLevel Iteration (AMLI) method for systems arising from discretizations of parabolic problems, using Crouzeix-Raviart finite elements. The developed AMLI method is based on an approximated block factorization of the original system matrix, where the partitioning is associated with a sequence of nested discretization meshes. In the second part of the thesis we develop solution methods for the numerical simulation of multiphase flow problems, modelled by the Cahn-Hilliard (C-H) equation. We consider the discrete C-H problem, obtained via finite element discretization in space and implicit schemes in time. We propose techniques to precondition the Jacobian of the discrete nonlinear system, based on its natural two-by-two block structure. The preconditioners are used in the framework of inexact Newton methods. We develop two nonlinear solution algorithms for the Cahn-Hilliard problem. Both lead to efficient optimal order methods. One of the main advantages of the proposed methods is that they are implemented using available software toolboxes for both sequential and distributed execution. The theoretical analysis of the solution methods presented in this thesis is combined with numerical studies that confirm their efficiency.

Guaranteed Minimum Withdrawal Benefits(GMWB) have become popular riders on variable annuities. The pricing of a GMWB contract was originally formulated as a singular stochastic control problem which results in a Hamilton Jacobi Bellman (HJB) Variational Inequality (VI). A penalty method method can then be used to solve the HJB VI. We present a rigorous proof of convergence of the penalty method to the viscosity solution of the HJB VI assuming the underlying asset follows a Geometric Brownian Motion. A direct control method is an alternative formulation for the HJB VI. We also extend the HJB VI to the case of where the underlying asset follows a Poisson jump diffusion. The HJB VI is normally solved numerically by an implicit method, which gives rise to highly nonlinear discretized algebraic equations. The classic policy iteration approach works well for the Geometric Brownian Motion case. However it is not efficient in some circumstances such as when the underlying asset follows a Poisson jump diffusion process. We develop a combined fixed point policy iteration scheme which significantly increases the efficiency of solving the discretized equations. Sufficient conditions to ensure the convergence of the combined fixed point policy iteration scheme are derived both for the penalty method and direct control method. The GMWB formulated as a singular control problem has a special structure which results in a block matrix fixed point policy iteration converging about one order of magnitude faster than a full matrix fixed point policy iteration. Sufficient conditions for convergence of the block matrix fixed point policy iteration are derived. Estimates for bounds on the penalty parameter (penalty method) and scaling parameter (direct control method) are obtained so that convergence of the iteration can be expected in the presence of round-off error.

Over the last 25 years, interior-point methods (IPMs) have emerged as a viable class of algorithms for solving various forms of conic optimization problems. Most IPMs use a modified Newton method to determine the search direction at each iteration. The system of equations corresponding to the modified Newton system can often be reduced to the so-called normal equation, a system of equations whose matrix ADA' is positive definite, yet often ill-conditioned. In this thesis, we first investigate the theoretical properties of the maximum weight basis (MWB) preconditioner, and show that when applied to a matrix of the form ADA', where D is positive definite and diagonal, the MWB preconditioner yields a preconditioned matrix whose condition number is uniformly bounded by a constant depending only on A. Next, we incorporate the results regarding the MWB preconditioner into infeasible, long-step, primal-dual, path-following algorithms for linear programming (LP) and convex quadratic programming (CQP). In both LP and CQP, we show that the number of iterative solver iterations of the algorithms can be uniformly bounded by n and a condition number of A, while the algorithmic iterations of the IPMs can be polynomially bounded by n and the logarithm of the desired accuracy. We also expand the scope of the LP and CQP algorithms to incorporate a family of preconditioners, of which MWB is a member, to determine an approximate solution to the normal equation. For the remainder of the thesis, we develop a new preconditioning strategy for solving systems of equations whose associated matrix is positive definite but ill-conditioned. Our so-called adaptive preconditioning strategy allows one to change the preconditioner during the course of the conjugate gradient (CG) algorithm by post-multiplying the current preconditioner by a simple matrix, consisting of the identity matrix plus a rank-one update. Our resulting algorithm, the Adaptive Preconditioned CG (APCG) algorithm, is shown to have polynomial convergence properties. Numerical tests are conducted to compare a variant of the APCG algorithm with the CG algorithm on various matrices.