São analisados nesta dissertação problemas em teoria dos jogos, com enfoque no efeito que perturbações acarretam em jogos. A análise suavizada (smoothed analysis) é utilizada para tal análise, e dois tipos de jogos são o foco principal desta dissertação, jogos bimatrizes e o problema de atribuição de tráfego (Traffic Assignment Problem.) O algoritmo de Lemke-Howson é um método utilizado amplamente para computar um equilíbrio Nash de jogos bimatrizes. Esse problema é PPAD-completo (Polynomial Parity Arguments on Directed graphs), e existem instâncias em que um tempo exponencial é necessário para terminar o algoritmo. Mesmo utilizando análise suavizada, esse problema permanece exponencial. Entretanto, nenhum estudo experimental foi realizado para demonstrar na prática como o algoritmo se comporta em casos com perturbação. Esta dissertação demonstra como as instâncias de pior caso conhecidas atualmente podem ser geradas e mostra que a performance do algoritmo nestas instâncias, quando perturbações são aplicadas, difere do comportamento esperado provado pela teoria. O Problema de Atribuição de Tráfego modela situações em uma rede viária onde usuários necessitam viajar de um nodo origem a um nodo destino. Esse problema pode ser modelado como um jogo, usando teoria dos jogos, onde um equilíbrio Nash acontece quando os usuários se comportam de forma egoísta. O custo total ótimo corresponde ao melhor fluxo de um ponto de vista global. Nesta dissertação, uma nova medida de perturbação é apresentada, o Preço da Anarquia Suavizado (Smoothed Price of Anarchy), baseada na análise suavizada de algoritmos, com o fim de analisar os efeitos da perturbação no Preço da Anarquia. Usando esta medida, são estudados os efeitos que perturbações têm no Preço da Anarquia para instâncias reais e teóricas para o Problema de Atribuição de Tráfego. É demonstrado que o Preço da Anarquia Suavizado se mantém na mesma ordem do Preço da Anarquia sem perturbações para funções de latência polinomiais. Finalmente, são estudadas instâncias de benchmark em relação à perturbação. / This thesis analyzes problems in game theory with respect to perturbation. It uses smoothed analysis to accomplish such task and focuses on two kind of games, bimatrix games and the traffic assignment problem. The Lemke-Howson algorithm is a widely used algorithm to compute a Nash equilibrium of a bimatrix game. This problem is PPAD-complete (Polynomial Parity Arguments on Directed graphs), and there exists an instance which takes exponential time (with any starting pivot.) It has been proven that even with a smoothed analysis it is still exponential. However, no experimental study has been done to verify and evaluate in practice how the algorithm behaves in such cases. This thesis shows in detail how the current known worst-case instances are generated and shows that the performance of the algorithm on these instances, when perturbed, differs from the expected behavior proven in theory. The Traffic Assignment Problem models a situation in a road network where users want to travel from an origin to a destination. It can be modeled as a game using game theory, with a Nash equilibrium happening when users behave selfishly and an optimal social welfare being the best possible flow from a global perspective. We provide a new measure, which we call the Smoothed Price of Anarchy, based on the smoothed analysis of algorithms in order to analyze the effects of perturbation on the Price of Anarchy. Using this measure, we analyze the effects that perturbation has on the Price of Anarchy for real and theoretical instances for the Traffic Assignment Problem. We demonstrate that the Smoothed Price of Anarchy remains in the same order as the original Price of Anarchy for polynomial latency functions. Finally, we study benchmark instances in relation to perturbation.

São analisados nesta dissertação problemas em teoria dos jogos, com enfoque no efeito que perturbações acarretam em jogos. A análise suavizada (smoothed analysis) é utilizada para tal análise, e dois tipos de jogos são o foco principal desta dissertação, jogos bimatrizes e o problema de atribuição de tráfego (Traffic Assignment Problem.) O algoritmo de Lemke-Howson é um método utilizado amplamente para computar um equilíbrio Nash de jogos bimatrizes. Esse problema é PPAD-completo (Polynomial Parity Arguments on Directed graphs), e existem instâncias em que um tempo exponencial é necessário para terminar o algoritmo. Mesmo utilizando análise suavizada, esse problema permanece exponencial. Entretanto, nenhum estudo experimental foi realizado para demonstrar na prática como o algoritmo se comporta em casos com perturbação. Esta dissertação demonstra como as instâncias de pior caso conhecidas atualmente podem ser geradas e mostra que a performance do algoritmo nestas instâncias, quando perturbações são aplicadas, difere do comportamento esperado provado pela teoria. O Problema de Atribuição de Tráfego modela situações em uma rede viária onde usuários necessitam viajar de um nodo origem a um nodo destino. Esse problema pode ser modelado como um jogo, usando teoria dos jogos, onde um equilíbrio Nash acontece quando os usuários se comportam de forma egoísta. O custo total ótimo corresponde ao melhor fluxo de um ponto de vista global. Nesta dissertação, uma nova medida de perturbação é apresentada, o Preço da Anarquia Suavizado (Smoothed Price of Anarchy), baseada na análise suavizada de algoritmos, com o fim de analisar os efeitos da perturbação no Preço da Anarquia. Usando esta medida, são estudados os efeitos que perturbações têm no Preço da Anarquia para instâncias reais e teóricas para o Problema de Atribuição de Tráfego. É demonstrado que o Preço da Anarquia Suavizado se mantém na mesma ordem do Preço da Anarquia sem perturbações para funções de latência polinomiais. Finalmente, são estudadas instâncias de benchmark em relação à perturbação. / This thesis analyzes problems in game theory with respect to perturbation. It uses smoothed analysis to accomplish such task and focuses on two kind of games, bimatrix games and the traffic assignment problem. The Lemke-Howson algorithm is a widely used algorithm to compute a Nash equilibrium of a bimatrix game. This problem is PPAD-complete (Polynomial Parity Arguments on Directed graphs), and there exists an instance which takes exponential time (with any starting pivot.) It has been proven that even with a smoothed analysis it is still exponential. However, no experimental study has been done to verify and evaluate in practice how the algorithm behaves in such cases. This thesis shows in detail how the current known worst-case instances are generated and shows that the performance of the algorithm on these instances, when perturbed, differs from the expected behavior proven in theory. The Traffic Assignment Problem models a situation in a road network where users want to travel from an origin to a destination. It can be modeled as a game using game theory, with a Nash equilibrium happening when users behave selfishly and an optimal social welfare being the best possible flow from a global perspective. We provide a new measure, which we call the Smoothed Price of Anarchy, based on the smoothed analysis of algorithms in order to analyze the effects of perturbation on the Price of Anarchy. Using this measure, we analyze the effects that perturbation has on the Price of Anarchy for real and theoretical instances for the Traffic Assignment Problem. We demonstrate that the Smoothed Price of Anarchy remains in the same order as the original Price of Anarchy for polynomial latency functions. Finally, we study benchmark instances in relation to perturbation.

São analisados nesta dissertação problemas em teoria dos jogos, com enfoque no efeito que perturbações acarretam em jogos. A análise suavizada (smoothed analysis) é utilizada para tal análise, e dois tipos de jogos são o foco principal desta dissertação, jogos bimatrizes e o problema de atribuição de tráfego (Traffic Assignment Problem.) O algoritmo de Lemke-Howson é um método utilizado amplamente para computar um equilíbrio Nash de jogos bimatrizes. Esse problema é PPAD-completo (Polynomial Parity Arguments on Directed graphs), e existem instâncias em que um tempo exponencial é necessário para terminar o algoritmo. Mesmo utilizando análise suavizada, esse problema permanece exponencial. Entretanto, nenhum estudo experimental foi realizado para demonstrar na prática como o algoritmo se comporta em casos com perturbação. Esta dissertação demonstra como as instâncias de pior caso conhecidas atualmente podem ser geradas e mostra que a performance do algoritmo nestas instâncias, quando perturbações são aplicadas, difere do comportamento esperado provado pela teoria. O Problema de Atribuição de Tráfego modela situações em uma rede viária onde usuários necessitam viajar de um nodo origem a um nodo destino. Esse problema pode ser modelado como um jogo, usando teoria dos jogos, onde um equilíbrio Nash acontece quando os usuários se comportam de forma egoísta. O custo total ótimo corresponde ao melhor fluxo de um ponto de vista global. Nesta dissertação, uma nova medida de perturbação é apresentada, o Preço da Anarquia Suavizado (Smoothed Price of Anarchy), baseada na análise suavizada de algoritmos, com o fim de analisar os efeitos da perturbação no Preço da Anarquia. Usando esta medida, são estudados os efeitos que perturbações têm no Preço da Anarquia para instâncias reais e teóricas para o Problema de Atribuição de Tráfego. É demonstrado que o Preço da Anarquia Suavizado se mantém na mesma ordem do Preço da Anarquia sem perturbações para funções de latência polinomiais. Finalmente, são estudadas instâncias de benchmark em relação à perturbação. / This thesis analyzes problems in game theory with respect to perturbation. It uses smoothed analysis to accomplish such task and focuses on two kind of games, bimatrix games and the traffic assignment problem. The Lemke-Howson algorithm is a widely used algorithm to compute a Nash equilibrium of a bimatrix game. This problem is PPAD-complete (Polynomial Parity Arguments on Directed graphs), and there exists an instance which takes exponential time (with any starting pivot.) It has been proven that even with a smoothed analysis it is still exponential. However, no experimental study has been done to verify and evaluate in practice how the algorithm behaves in such cases. This thesis shows in detail how the current known worst-case instances are generated and shows that the performance of the algorithm on these instances, when perturbed, differs from the expected behavior proven in theory. The Traffic Assignment Problem models a situation in a road network where users want to travel from an origin to a destination. It can be modeled as a game using game theory, with a Nash equilibrium happening when users behave selfishly and an optimal social welfare being the best possible flow from a global perspective. We provide a new measure, which we call the Smoothed Price of Anarchy, based on the smoothed analysis of algorithms in order to analyze the effects of perturbation on the Price of Anarchy. Using this measure, we analyze the effects that perturbation has on the Price of Anarchy for real and theoretical instances for the Traffic Assignment Problem. We demonstrate that the Smoothed Price of Anarchy remains in the same order as the original Price of Anarchy for polynomial latency functions. Finally, we study benchmark instances in relation to perturbation.

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Structural algorithms incorporate the interdependence of outputs into the prediction, the loss, or both. Frank-Wolfe optimizations of pairwise losses and Gaussian conditional random fields for multivariate output regression are two such structural algorithms. Pairwise losses are standard 0-1 classification surrogate losses applied to pairs of features and outputs, resulting in improved ranking performance (area under the ROC curve, average precision, and F-1 score) at the cost of increased learning complexity. In this dissertation, it is proven that the balanced loss 0-1 SVM and the pairwise SVM have the same dual loss and the pairwise dual coefficient domain is a subdomain of the balanced loss 0-1 SVM with bias dual coefficient domain. This provides a theoretical advancement in the understanding of pairwise loss, which we exploit for the development of a novel ranking algorithm that is fast and memory efficient method with state the art ranking metric performance across eight benchmark data sets. Various practical advancements are also made in multivariate output regression. The learning time for Gaussian conditional random fields is greatly reduced and the parameter domain is expanded to enable repulsion between outputs. Last, a novel multivariate regression is presented that keeps the desirable elements of GCRF and infuses them into a local regression model that improves mean squared error and reduces learning complexity. / Computer and Information Science

The increasing penetration of proactive agents in distribution systems (DS) has opened new possibilities to make the grid more resilient and to increase participation of responsive loads (RL) and non-conventional generation resources. On the resiliency side, plug-in hybrid electric vehicles (PHEV), energy storage systems (ESS), microgrids (MG), and distributed energy resources (DER), can be leveraged to restore critical load in the system when the utility system is not available for extended periods of time. Critical load restoration is a key factor to achieve a resilient distribution system. On the other hand, existing DERs and responsive loads can be coordinated in a market environment to contribute to efficiency of electricity consumption and fair electricity tariffs, incentivizing proactive agents' participation in the distribution system. Resiliency and market applications for distribution systems are highly complex decision-making problems that can be addressed using modern optimization techniques. Complexities of these problems arise from non-linear relations, integer decision variables, scalability, and asynchronous information. On the resiliency side, existing models include optimization approaches that consider system's available information and neglect asynchrony of data arrival. As a consequence, these models can lead to underutilization of critical resources during system restoration. They can also become computationally intractable for large-scale systems. In the market design problem, existing approaches are based on centralized or computational distributed approaches that are not only limited by hardware requirements but also restrictive for active participation of the market agents. In this context, the work of this dissertation results in major contributions regarding new optimization algorithms for market design and resiliency improvement in distribution systems. In the DS market side, two novel contribution are presented: 1) A computational distributed coordination framework based on bilateral transactions where social welfare is maximized, and 2) A fully decentralized transactive framework where power suppliers, in a simultaneous auction environment, strategically bid using a Markowitz portfolio optimization approach. On the resiliency side, this research proposed a system restoration approach, taking into account uncertain devices and associated asynchronous information, by means of a two-module optimization models based on binary programming and three phase unbalanced optimal power flow. Furthermore, a Reinforcement Learning (RL) method along with a Monte Carlo tree search algorithm has been proposed to solve the scalability problem for resiliency enhancement. / Doctor of Philosophy / Distribution systems (DS) are evolving from traditional centralized and fossil fuel generation resources to networks with large scale deployment of responsive loads and distributed energy resources. Optimization-based decision-making methods to improve resiliency and coordinate DS participants are required. Prohibitive costs due to extended power outages require efficient mechanisms to avoid interruption of service to critical load during catastrophic power outages. Coordination mechanisms for various generation resources and proactive loads are in great need. Existing optimization-based approaches either neglect the asynchronous nature of the information arrival or are computationally intractable for large scale system. The work of this dissertation results in major contributions regarding new optimization methods for market design, coordination of DS participants, and improvement of DS resiliency. Four contributions toward the application of optimization approaches for DS are made: 1) A distributed optimization algorithm based on decomposition and best approximation techniques to maximize social welfare in a market environment, 2) A simultaneous auction mechanism and portfolio optimization method in a fully decentralized market framework, 3) Binary programming and nonlinear unbalanced power flow, considering asynchronous information, to enhance resiliency in a DS, and 4) A reinforcement learning method together with an efficient search algorithm to support large scale resiliency improvement models incorporating asynchronous information.

A typical problem in traffic control is the steering over a network of vehicles with different origins and destinations. In this report this scenario is formulated as a multi-commodity network flow problem, a linear programming problem whose objective is to transport, with minimum cost, different commodities from their respective sources to their sinks through a network, while respecting the capacity constraints of the roads. The dynamic network flow formulation of the problem is also presented, extending the network over time to incorporate the temporal dimension. Different algorithms for solving the multi-commodity network flow problem are examined. First, the simplex method, more precisely its revised version, is considered, and then the Dantzig-Wolfe decomposition is illustrated, an optimization algorithm which exploits specific block structures in the constraints. These methods are applied using state-of-the-art linear programming solvers and evaluated with a simulation based on the road network in central Stockholm. The results show that both methods allow for solving the traffic flow problem, with limitations given by the specifics of the solvers and by the space and time discretization of the problem. In particular, the revised simplex algorithm results the faster method.

Cette thèse porte sur l'utilisation du BLASSO, un problème d'optimisation convexe en dimension infinie généralisant le LASSO aux mesures, pour la super-résolution de sources ponctuelles. Nous montrons d'abord que la stabilité du support des solutions, pour N sources se regroupant, est contrôlée par un objet appelé pré-certificat aux 2N-1 dérivées nulles. Quand ce pré-certificat est non dégénéré, dans un régime de petit bruit dont la taille est contrôlée par la distance minimale séparant les sources, le BLASSO reconstruit exactement le support de la mesure initiale. Nous proposons ensuite l'algorithme Sliding Frank-Wolfe, une variante de l'algorithme de Frank-Wolfe avec déplacement continu des amplitudes et des positions, qui résout le BLASSO. Sous de faibles hypothèses, cet algorithme converge en un nombre fini d'itérations. Nous utilisons cet algorithme pour un problème 3D de microscopie par fluorescence en comparant trois modèles construits à partir des techniques PALM/STORM. / This thesis studies the noisy sparse spikes super-resolution problem for positive measures using the BLASSO, an infinite dimensional convex optimization problem generalizing the LASSO to measures. First, we show that the support stability of the BLASSO for N clustered spikes is governed by an object called the (2N-1)-vanishing derivatives pre-certificate. When it is non-degenerate, solving the BLASSO leads to exact support recovery of the initial measure, in a low noise regime whose size is controlled by the minimal separation distance of the spikes. In a second part, we propose the Sliding Frank-Wolfe algorithm, based on the Frank-Wolfe algorithm with an added step moving continuously the amplitudes and positions of the spikes, that solves the BLASSO. We show that, under mild assumptions, it converges in a finite number of iterations. We apply this algorithm to the 3D fluorescent microscopy problem by comparing three models based on the PALM/STORM technics.

L'objectif de ce mémoire est de rendre compte, en les comparant, des discours publics respectifs de Theobald Wolfe Tone, pour l'Irlande, et de Louis-Joseph Papineau pour le Bas-Canada. Il s'agit de mener cette analyse pour les années de la montée des revendications constitutionnelles et de l'évocation de l'indépendance dans deux territoires politiquement dépendants de la Grande-Bretagne, fin du 18e siècle (1790-1798) et début du 19e siècle (1827-1837). Ces années ont précédé, respectivement, la Rébellion de 1798 (Irlande et l'Union à la Grande-Bretagne, en 1801), et celle de 1837 au Bas-Canada, suivie de l'Union au Haut-Canada, en 1840. De ce point de vue, les grands thèmes retenus pour l'analyse comparative sont naturellement ceux de la dépendance et de la constitution. À partir des écrits publics de Tone, des retranscriptions des discours de Papineau à la Chambre d'assemblée du Bas-Canada et de ses brochures, ce mémoire propose une analyse du discours public de ces personnages qui ont désiré changer le destin politique de leur «pays» respectif. En Irlande, Theobald Wolfe Tone apparaît d'abord comme un whig progressiste aspirant faire une carrière de parlementaire. Il sera actif politiquement hors du Parlement et publiciste. Au Bas-Canada, Louis-Joseph Papineau sera président de la Chambre d'assemblée coloniale du Bas-Canada de 1815 à 1837. D'abord partisans d'une application autonomiste d'une constitution britannique récente (1782 pour l'Irlande) et relativement récente (1791 pour le Bas-Canada), ils éprouvent ensuite les limites du possible dans l'ordre établi par ces constitutions, puis croient nécessaire de sortir de cet ordre constitutionnel par l'indépendance. Comme réformistes, ils arrivent tous deux à l'idée que le changement n'est possible qu'avec des modifications à la constitution elle-même: Tone prône l'unité de tous les Irlandais, l'«émancipation des catholiques», qui sont en Irlande l'objet d'une discrimination politique légalement instituée et l'application de l'autonomie législative. Papineau pour sa part se fait au Bas-Canada le défenseur de l'idée de l'électivité d'un Conseil législatif, alors dépendant du Conseil exécutif, du gouverneur et du gouvernement métropolitain, et concurrent de l'Assemblée. Ces réformes ne pouvant se réaliser, Tone et Papineau en arrivent à la nécessité, comme moyen, de l'indépendance. Tone la conçoit comme devant se faire immédiatement, par la voie révolutionnaire du recours aux armes et la collaboration de la France. Papineau fait plutôt l'apologie, pour l'immédiat, de mesures «paisibles, légales et constitutionnelles», mais il vante dès les années 1830 la supériorité des institutions américaines et ne se désolidarise pas publiquement de ceux qui en 1837 désirent une résistance armée. Cette recherche a montré que le discours public de Tone constitue d'abord un plaidoyer pour l'autonomie législative de l'Irlande, et que celui de Papineau affirme les prérogatives de l'Assemblée élue dans l'équilibre des pouvoirs au Bas-Canada. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Autodétermination, Constitution, Émancipation, Institutions coloniales, Nation, Rébellion, Réforme, 18e siècle, 19e siècle.

There are many real cases where a company needs to determine the price of its products so as to maximise its revenue or profit.<p>To do so, the company must consider customers' reactions to these prices, as they may refuse to buy a given product or service if its price is too high. This is commonly known in literature as a pricing problem.<p>This class of problems, which is typically bilevel, was first studied in the 1990s and is NP-hard, although polynomial algorithms do exist for some particular cases. Many questions are still open on this subject.<p><p>The aim of this thesis is to investigate mathematical properties of pricing problems, in order to find structural properties, formulations and solution methods that are as efficient as possible. In particular, we focus our attention on pricing problems over a network. In this framework, an authority owns a subset of arcs and imposes tolls on them, in an attempt to maximise his/her revenue, while users travel on the network, seeking for their minimum cost path.<p><p>First, we provide a detailed review of the state of the art on bilevel pricing problems. <p>Then, we consider a particular case where the authority is using an unit toll scheme on his/her subset of arcs, imposing either the same toll on all of them, or a toll proportional to a given parameter particular to each arc (for instance a per kilometre toll). We show that if tolls are all equal then the complexity of the problem is polynomial, whereas in case of proportional tolls it is pseudo-polynomial.<p>We then address a robust approach taking into account uncertainty on parameters. We solve some polynomial cases of the pricing problem where uncertainty is considered using an interval representation.<p><p>Finally, we focus on another particular case where toll arcs are connected such that they constitute a path, as occurs on highways. We develop a Dantzig-Wolfe reformulation and present a Branch-and-Cut-and-Price algorithm to solve it. Several improvements are proposed, both for the column generation algorithm used to solve the linear relaxation and for the branching part used to find integer solutions. Numerical results are also presented to highlight the efficiency of the proposed strategies. This problem is proved to be APX-hard and a theoretical comparison between our model and another one from the literature is carried out. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished