Dans cette thèse nous étudions une classe d’équations de Hamilton-Jacobi-Bellman provenant de la théorie du contrôle optimal des équations différentielles ordinaires. Nous nous intéressons principalement à l’analyse de la sensibilité de la fonction valeur des problèmes de contrôle optimal associés à de telles équations de H-J-B. Dans la littérature, les relations de sensibilité fournissent une “mesure” de la robustesse des stratégies optimales par rapport aux variations de la variable d’état. Ces résultats sont des outils très importants pour le contrôle appliqué, parce qu’ils permettent d’étudier les effets que des approximations des données du système peuvent avoir sur les politiques optimales. Cette thèse est dédiée également à l’étude des problèmes de Mayer et de temps minimal. Nous supposons que la dynamique du problème soit une inclusion différentielle, afin de permettre aux données d’être non régulières et d’embrasser un ensemble plus grand d’applications. Néanmoins, cette tâche rend notre analyse plus difficile. La première contribution de cette étude est une extension de quelques résultats classiques de la théorie de la sensibilité au domaine des problèmes non paramétrées. Ces relations prennent la forme d’inclusions d’état adjoint, figurant dans le principe du maximum de Pontryagin, dans certains gradients généralisés de la fonction valeur évalués le long des trajectoires optimales. En deuxième lieu, nous développons des nouvelles relations de sensibilité impliquant des approximations du deuxième ordre de la fonction valeur. Cette analyse mène à de nouvelles applications concernant la propagation, tant ponctuel que local, de la régularité de la fonction valeur le long des trajectoires optimales. Nous proposons également des applications aux conditions d’optimalité. / This dissertation investigates a class of Hamilton-Jacobi-Bellman equations arising in optimal control of O.D.E.. We mainly focus on the sensitivity analysis of the optimal value function associated with the underlying control problems. In the literature, sensitivity relations provide a measure of the robustness of optimal control strategies with respect to variations of the state variable. This is a central tool in applied control, since it allows to study the effects that approximations of the inputs of the system may produce on the optimal policies. In this thesis, we deal whit problems in the Mayer or in the minimum time form. We assume that the dynamic is described by a differential inclusion, in order to allow data to be nonsmooth and to embrace a large area of concrete applications. Nevertheless, this task makes our analysis more challenging. Our main contribution is twofold. We first extend some classical results on sensitivity analysis to the field of nonparameterized problems. These relations take the form of inclusions of the co-state, featuring in the Pontryagin maximum principle, into suitable gradients of the value function evaluated along optimal trajectories. Furthermore, we develop new second-order sensitivity relations involving suitable second order approximations of the optimal value function. Besides being of intrinsic interest, this analysis leads to new consequences regarding the propagation of both pointwise and local regularity of the optimal value functions along optimal trajectories. As applications, we also provide refined necessary optimality conditions for some class of differential inclusions.

Cette thèse se développe sur deux fronts: nous nous concentrons sur les méthodes numériques des problèmes de contrôle optimal, en particulier sur le Principe de la Programmation Dynamique et sur le Model Predictive Control (MPC) et nous présentons des applications de techniques de contrôle en biologie. Dans la première partie, nous considérons l'approximation d'un problème de contrôle optimal avec horizon infini, qui combine une première étape, basée sur MPC permettant d'obtenir rapidement une bonne approximation de la trajectoire optimal, et une seconde étape, dans la quelle l¿équation de Bellman est résolue dans un voisinage de la trajectoire de référence. De cette façon, on peux réduire une grande partie de la taille du domaine dans lequel on résout l¿équation de Bellman et diminuer la complexité du calcul. Le deuxième sujet est le contrôle des méthodes Level Set: on considère un problème de contrôle optimal, dans lequel la dynamique est donnée par la propagation d'un graphe à une dimension, contrôlé par la vitesse normale. Un état finale est fixé, l'objectif étant de le rejoindre en minimisant une fonction coût appropriée. On utilise la programmation dynamique grâce à une réduction d'ordre de l'équation utilisant la Proper Orthogonal Decomposition. La deuxième partie est dédiée à l'application des méthodes de contrôle en biologie. On présente un modèle décrit par une équation aux dérivées partielles qui modélise l'évolution d'une population de cellules tumorales. On analyse les caractéristiques du modèle et on formule et résout numériquement un problème de contrôle optimal concernant ce modèle, où le contrôle représente la quantité du médicament administrée. / This thesis is divided in two parts: in the first part we focus on numerical methods for optimal control problems, in particular on the Dynamic Programming Principle and on Model Predictive Control (MPC), in the second part we present some applications of the control techniques in biology. In the first part of the thesis, we consider the approximation of an optimal control problem with an infinite horizon, which combines a first step based on MPC, to obtain a fast but rough approximation of the optimal trajectory and a second step where we solve the Bellman equation in a neighborhood of the reference trajectory. In this way, we can reduce the size of the domain in which the Bellman equation can be solved and so the computational complexity is reduced as well. The second topic of this thesis is the control of the Level Set methods: we consider an optimal control, in which the dynamics is given by the propagation of a one dimensional graph, which is controlled by the normal velocity. A final state is fixed and the aim is to reach the trajectory chosen as a target minimizing an appropriate cost functional. To apply the Dynamic Programming approach we firstly reduce the size of the system using the Proper Orthogonal Decomposition. The second part of the thesis is devoted to the application of control methods in biology. We present a model described by a partial differential equation that models the evolution of a population of tumor cells. We analyze the mathematical and biological features of the model. Then we formulate an optimal control problem for this model and we solve it numerically.

This study examines how smells were perceived in 18th century Stockholm by analyzing odor references in the poetry collection Fredman’s Epistles (1790) (Sw. Fredmans epistlar) by Carl Michael Bellman (1740–1795). Using a specific form of contextualizing, the purpose of the study is to show how the meaning of smells in 18th century Stockholm appears when the smelling objects in the epistles are put in a relevant context. By extension, the investigation will show how the smell sensations in the epistles reveal attitudes towards life, death and certain social and cultural phenomena. I argue that the inhabitants of Stockholm in the 18th century, depending on their social and cultural belonging, shared many of the attitudes towards places, people and phenomena that are reproduced in the epistles. I also argue that 18th century Stockholm was not as dominated by foul smells as has been claimed by previous research. This study sheds new light on how different smells were perceived, described and understood in 18th century Stockholm.

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We propose a discontinuous Galerkin finite element method (DGFEM) for fully nonlinear elliptic Hamilton--Jacobi--Bellman (HJB) partial differential equations (PDE) of second order with Cordes coefficients. Our analysis shows that the method is both consistent and stable, with arbitrarily high-order convergence rates for sufficiently regular solutions. Error bounds for solutions with minimal regularity show that the method is generally convergent under suitable choices of meshes and polynomial degrees. The method allows for a broad range of hp-refinement strategies on unstructured meshes with varying element sizes and orders of approximation, thus permitting up to exponential convergence rates, even for nonsmooth solutions. Numerical experiments on problems with nonsmooth solutions and strongly anisotropic diffusion coefficients demonstrate the significant gains in accuracy and computational efficiency over existing methods. We then extend the DGFEM for elliptic HJB equations to a space-time DGFEM for parabolic HJB equations. The resulting method is consistent and unconditionally stable for varying time-steps, and we obtain error bounds for both rough and regular solutions, which show that the method is arbitrarily high-order with optimal convergence rates with respect to the mesh size, time-step size, and temporal polynomial degree, and possibly suboptimal by an order and a half in the spatial polynomial degree. Exponential convergence rates under combined hp- and τq-refinement are obtained in numerical experiments on problems with strongly anisotropic diffusion coefficients and early-time singularities. Finally, we show that the combination of a semismooth Newton method with nonoverlapping domain decomposition preconditioners leads to efficient solvers for the discrete nonlinear problems. The semismooth Newton method has a superlinear convergence rate, and performs very effectively in computations. We analyse the spectral bounds of nonoverlapping domain decomposition preconditioners for a model problem, where we establish sharp bounds that are explicit in both the mesh sizes and polynomial degrees. We then go beyond the model problem and show computationally that these algorithms lead to efficient and competitive solvers in practical applications to fully nonlinear HJB equations.

The presented thesis project is a study on train energy consumption calculation and optimal train driving strategies for minimum energy consumption. This study is divided into three parts; the first part is a proposed model for energy consumption calculation for trains based on driving resistances. The second part is a presentation of a method based on dynamic programming and the Hamilton-Jacobi-Bellman equation (Bellman’s backward approach) for obtaining optimal speed and control profiles leading to minimum energy consumption. The third part is a case study for a Bombardier Transportation case. It includes the presentation of a preliminary algorithm developed within this thesis project; an algorithm based on the HJB equation that can be further improved in order to be used online in real-time as an advisory system for train drivers.

Artificial vision is the problem of creating systems capable of processing visual information. A fundamental sub-problem of artificial vision is image segmentation, the problem of detecting a structure from a digital image. Examples of segmentation problems include the detection of a road from an aerial photograph or the determination of the boundaries of the brain's ventricles from medical imagery. The extraction of structures allows for subsequent higher-level cognitive tasks. One of them is shape comparison. For example, if the brain ventricles of a patient are segmented, can their shapes be used for diagnosis? That is to say, do the shapes of the extracted ventricles resemble more those of healthy patients or those of patients suffering from schizophrenia? This thesis deals with the problem of image segmentation and shape comparison in the mathematical framework of partial differential equations. The contribution of this thesis is threefold: 1. A technique for the segmentation of regions is proposed. A cost functional is defined for regions based on a non-parametric functional of the distribution of image intensities inside the region. This cost is constructed to favor regions that are homogeneous. Regions that are optimal with respect to that cost can be determined with limited user interaction. 2. The use of direction information is introduced for the segmentation of open curves and closed surfaces. A cost functional is defined for structures (curves or surfaces) by integrating a local, direction-dependent pattern detector along the structure. Optimal structures, corresponding to the best match with the pattern detector, can be determined using efficient algorithms. 3. A technique for shape comparison based on the Laplace equation is proposed. Given two surfaces, one-to-one correspondences are determined that allow for the characterization of local and global similarity measures. The local differences among shapes (resulting for example from a segmentation step) can be visualized for qualitative evaluation by a human expert. It can also be used for classifying shapes into, for example, normal and pathological classes.

A continuous service innovation such as Cloud Computing is highly attractive in the business-to-business world because it brings the service provider both billions of dollars in profits and superior competitive advantage. The success of such an innovation is strongly tied to a consumer’s adoption decision. When dealing with a continuous service innovation, the consumer’s decision process becomes complicated. Not only do consumers need to consider two different decisions of both whether to adopt and how long to adopt (contract length), but also the increasing trend of the service-related technological improvements invokes a forward-looking behavior in consumer’s decision process. Moreover, consumers have to balance the benefits and costs of adoption when evaluating decision alternatives. Consumer adoption decisions come with the desire to have the latest technology and the fear of the adopted technology becoming obsolete. Non-adoption prevents consumers from being locked-in by the service provider, but buying that technology may be costly. Being bound to a longer contract forfeits the opportunity to capitalize on the technological revolution. Frequently signing shorter contracts increases the non-physical efforts such as learning, training and negotiating costs. Targeting the right consumers at the right time with the right service offer in the business-to-business context requires an efficient strategy of sales resource allocation. This is a “mission impossible” for service providers if they do not know how consumers make decisions regarding service innovation. In order to guide the resource allocation decisions, we propose a complex model that integrates the structural, dynamic, and learning approaches to understand the consumer’s decision process on both whether or not to adopt, and how long to adopt a continuously updating service innovation in a B2B context.

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A presente dissertação expõe o ambiente em que o Problema de Merton é construído e, baseando-se na bibliografia apresentada, constrói exemplos em softwares cujas especificidades podem colaborar na clareza da resolução. O software Matlab engloba as soluções numéricas, enquanto o software Maple é responsável pela solução de equações diferenciais ordinárias e parciais de forma simbólica. Apresenta-se modificações do Problema de Merton original como exercícios para melhor esclarecer os diferentes parâmetros abordados. Na seção final é apresentada a solução de viscosidade, uma alternativa quando a função valor não apresenta características desejáveis para a análise apresentada. / This dissertation explicit the environment which Merton’s problem is built, according to the presented bibliography, exemples are built in softwares whose specificity might help to clarify the solution. The Matlab software embraces numeric solutions, while Maple software is appropriate to solve ordinary and parcial differential equations in symbolic form. Some modifications are presented to Merton’s Problem as exercise to improve understanding on the variations adopted. On final section, viscosity solutions are presented as an alternative solution for when the value function does not possess the desirables properties that allow the analysis on focus.