Considere $N$ pontos distribuídos de forma aleatória e uniforme num hipercubo $d$-dimensional. Cada ponto representa um sítio num meio desordenado. Um caminhante explora este meio saltando para os sítios mais próximos, que não tenham sido visitados nos últimos $\\mu$ (memoria) passos, inclusive o próprio sítio. A trajetória do caminhante é composta de uma parte transiente e de uma parte periódica (ciclos). Neste caso, o viajante pode ou não explorar todos espaço disponível. A partir de uma memória crítica, ocorre uma transição entre os regimes de exploração localizado e estendido. Para sistemas unidimensionais, essa transição ocorre na memória crítica $\\mu_1=\\log_2{N}$. A regra determinista pode ser suavizada, a fim de considerar situações mais realistas, com a inclusão do parâmetro estocástico $T$ (temperatura). Agora, os movimentos do caminhante são definidos por uma função densidade de probabilidade (PDF) que é parametrizada por $T$ e por uma função custo, que cresce à medida que a distância entre os sítios cresce. A PDF é escolhida de forma a favorecer saltos para sítios mais próximos. Com o aumento da temperatura, o caminhante pode sair de ciclos e estender sua exploração. Aqui, nós apresentamos os estudos analíticos e numéricos sobre a influência da temperatura e da memória crítica na exploração de um meio desordenado unidimensional. / Consider $N$ sites randomly and uniformly distributed in a $d$-dimensional hypercube. A walker explores this disordered medium going to the nearest site, which has not been visited in the last $\\mu$ (memory) steps. The walker trajectory is composed of a transient part and a periodic part (cycles). In this case, travelers can or cannot explore all available space, given rise to a crossover at critical memory, for one-dimensional systems $\\mu_1=\\log_2{N}$, between localized and extended regimes. % as function of $\\mu$. The deterministic rule can be softened to consider more realistic situations with the inclusion of a stochastic parameter $T$ (temperature). In this case, the walker movement is defined by a probability density function (PDF) that is parameterized by $T$ and a cost function, which increases as the distance among sites increases. The PDF is chosen to favor hops to nearest sites. As the temperature increases, the walker can escape from cycles and extend the exploration. Here we report the analytical and numerical studies of the influence of the temperature and the critical memory in the exploration of a one-dimensional disordered system.

As the title already suggests the aim of the present work is to present Farkas - type results for inequality systems involving convex and/or non - convex functions. To be able to give the desired results, we treat optimization problems which involve convex and composed convex functions or non - convex functions like DC functions or fractions. To be able to use the fruitful Fenchel - Lagrange duality approach, to the primal problem we attach an equivalent problem which is a convex optimization problem. After giving a dual problem to the problem we initially treat, we provide weak necessary conditions which secure strong duality, i.e., the case when the optimal objective value of the primal problem coincides with the optimal objective value of the dual problem and, moreover, the dual problem has an optimal solution. Further, two ideas are followed. Firstly, using the weak and strong duality between the primal problem and the dual problem, we are able to give necessary and sufficient optimality conditions for the optimal solutions of the primal problem. Secondly, provided that no duality gap lies between the primal problem and its Fenchel - Lagrange - type dual we are able to demonstrate some Farkas - type results and thus to underline once more the connections between the theorems of the alternative and the theory of duality. One statement of the above mentioned Farkas - type results is characterized using only epigraphs of functions. We conclude our investigations by providing necessary and sufficient optimality conditions for a multiobjective programming problem involving composed convex functions. Using the well-known linear scalarization to the primal multiobjective program a family of scalar optimization problems is attached. Further to each of these scalar problems the Fenchel - Lagrange dual problem is determined. Making use of the weak and strong duality between the scalarized problem and its dual the desired optimality conditions are proved. Moreover, the way the dual problem of the scalarized problem looks like gives us an idea about how to construct a vector dual problem to the initial one. Further weak and strong vector duality assertions are provided.

Considere $N$ pontos distribuídos de forma aleatória e uniforme num hipercubo $d$-dimensional. Cada ponto representa um sítio num meio desordenado. Um caminhante explora este meio saltando para os sítios mais próximos, que não tenham sido visitados nos últimos $\\mu$ (memoria) passos, inclusive o próprio sítio. A trajetória do caminhante é composta de uma parte transiente e de uma parte periódica (ciclos). Neste caso, o viajante pode ou não explorar todos espaço disponível. A partir de uma memória crítica, ocorre uma transição entre os regimes de exploração localizado e estendido. Para sistemas unidimensionais, essa transição ocorre na memória crítica $\\mu_1=\\log_2{N}$. A regra determinista pode ser suavizada, a fim de considerar situações mais realistas, com a inclusão do parâmetro estocástico $T$ (temperatura). Agora, os movimentos do caminhante são definidos por uma função densidade de probabilidade (PDF) que é parametrizada por $T$ e por uma função custo, que cresce à medida que a distância entre os sítios cresce. A PDF é escolhida de forma a favorecer saltos para sítios mais próximos. Com o aumento da temperatura, o caminhante pode sair de ciclos e estender sua exploração. Aqui, nós apresentamos os estudos analíticos e numéricos sobre a influência da temperatura e da memória crítica na exploração de um meio desordenado unidimensional. / Consider $N$ sites randomly and uniformly distributed in a $d$-dimensional hypercube. A walker explores this disordered medium going to the nearest site, which has not been visited in the last $\\mu$ (memory) steps. The walker trajectory is composed of a transient part and a periodic part (cycles). In this case, travelers can or cannot explore all available space, given rise to a crossover at critical memory, for one-dimensional systems $\\mu_1=\\log_2{N}$, between localized and extended regimes. % as function of $\\mu$. The deterministic rule can be softened to consider more realistic situations with the inclusion of a stochastic parameter $T$ (temperature). In this case, the walker movement is defined by a probability density function (PDF) that is parameterized by $T$ and a cost function, which increases as the distance among sites increases. The PDF is chosen to favor hops to nearest sites. As the temperature increases, the walker can escape from cycles and extend the exploration. Here we report the analytical and numerical studies of the influence of the temperature and the critical memory in the exploration of a one-dimensional disordered system.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Optimization problems are interesting both from the theoretical and practical point of view. In this thesis we address this subject, presenting problems of analytical nature, algebraic, geometric and combinatorial that can be addressed in basic education. Our main goal is to show how much content already taught in school can be used in attractive way for students through real-world problems can be solved with the use of mathematics. Also tried to suggest some topics that, although not part of the standard curriculum can be implemented by integrating diverse part. / Problemas de otimização são interessantes tanto do ponto de vista teórico quanto prático. Nesta dissertação abordamos este assunto, apresentando problemas de natureza analítica, algébrica, geométrica e combinatória que podem ser abordados no ensino básico. Nosso principal objetivo é evidenciar como muito dos conteúdos já ensinados na escola podem ser utilizados de forma atrativa para os alunos, através de problemas do cotidiano que podem ser resolvidos com o uso da matemática. Também experimentamos sugerir alguns temas que, embora não façam parte do currículo padrão, podem ser implementados integrando a parte diversificada.

Dans le domaine d’optimisation de forme de structures, la réduction des coûts et l’amélioration des produits sont des défis permanents à relever. Pour ce faire, le procédé de mise en forme doit être optimisé. Optimiser le procédé revient alors à résoudre un problème d’optimisation. Généralement ce problème est un problème d’optimisation multicritère très coûteux en terme de temps de calcul, où on cherche à minimiser plusieurs fonctions coût en présence d’un certain nombre de contraintes. Pour résoudre ce type de problème, on a développé un algorithme robuste, efficace et fiable. Cet algorithme, consiste à coupler un algorithme de capture de front de Pareto (NBI ou NNCM) avec un métamodèle (RBF), c’est-à-dire des approximations des résultats des simulations coûteuses. D’après l’ensemble des résultats obtenus par cette approche, il est intéressant de souligner que la capture de front de Pareto génère un ensemble des solutions non dominées. Pour savoir lesquelles choisir, le cas échéant, il est nécessaire de faire appel à des algorithmes de sélection, comme par exemple Nash et Kalai-Smorodinsky. Ces deux approches, issues de la théorie des jeux, ont été utilisées pour notre travail. L’ensemble des algorithmes sont validés sur deux cas industriels proposés par notre partenaire industriel. Le premier concerne un modèle 2D du fond de la canette (elasto-plasticité) et le second est un modèle 3D de la traverse (élasticité linéaire). Les résultats obtenus confirment l’efficacité de nos algorithmes développés. / One of the current challenges in the domain of the multiobjective shape optimization is to reduce the calculation time required by conventional methods. The high computational cost is due to the high number of simulation or function calls required by these methods. Recently, several studies have been led to overcome this problem by integratinga metamodel in the overall optimization loop. In this thesis, we perform a coupling between the Normal Boundary Intersection -NBI- algorithm and The Normalized Normal constraint Method -NNCM- algorithm with Radial Basis Function -RBF- metamodel in order to have asimple tool with a reasonable calculation time to solve multicriteria optimization problems. First, we apply our approach to academic test cases. Then, we validate our method against two industrial cases, namely, shape optimization of the bottom of a can undergoing nonlinear elasto-plastic deformation and an optimization of an automotive twist beam. Then, in order to select solutions among the Pareto efficient ones, we use the same surrogate approach to implement a method to compute Nash and Kalai-Smorodinsky equilibria.

Massive proliferation of wireless devices throughout world in the past decade comes with a host of tough and demanding design problems. Noise at receivers and wireless interference are the two major issues which severely limits the received signal quality and the quantity of users that can be simultaneously served. Traditional approaches to this problems are known as Power Control (PC), SINR Balancing (SINRB) and User Selection (US) in Wireless Networks respectively. Interestingly, for a large class of wireless system models, both this problems have a generic form. Thus any approach to this generic optimization problem benefits the transceiver design of all the underlying wireless models. In this thesis, we propose an Eigen approach based on the Joint Numerical Range (JNR) of hermitian matrices for PC, SINRB and US problems for a class of wireless models. In the beginning of the thesis, we address the PC and SINRB problems. PC problems can be expressed as Homogeneous Quadratic Constrained Quadratic Optimization Problems (HQCQP) which are known to be NP-Hard in general. Leveraging their connection to JNR, we show that when the constraints are fewer, HQCQP problems admit iterative schemes which are considerably fast compared to the state of the art and have guarantees of global convergence. In the general case for any number of constraints, we show that the true solution can be bounded above and below by two convex optimization problems. Our numerical simulations suggested that the bounds are tight in almost all scenarios suggesting the achievement of true solution. Further, the SINRB problems are shown to be intimately related to PC problems, and thus share the same approach. We then proceed on to comment on the convexity of PC problems and SINRB problems in the general case of any number of constraints. We show that they are intimately related to the convexity of joint numerical range. Based on this connection, we derive results on the attainability of solution and comment on the same about the state-of-the-art technique Semi-De nite Relaxation (SDR). In the subsequent part of the thesis, we address the US problem. We show that the US problem can be formulated as a combinatorial problem of selecting a feasible subset of quadratic constraints. We propose two approaches to the US problem. The first approach is based on the JNR view point which allows us to propose a heuristic approach. The heuristic approach is then shown to be equivalent to a convex optimization problem. In the second approach, we show that the US is equivalent to another non-convex optimization problem. We then propose a convex approximation approach to the latter. Both the approaches are shown to have near optimal performance in simulations. We conclude the thesis with a discussion on applicability and extension to other class of optimization problems and some open problems which has come out of this work.

Notre objectif dans cette thèse est de proposer des algorithmes efficaces pour résoudre des problèmes d’optimisation combinatoire difficiles. Dans un premier temps, nous établissons le principe de l’énumération ordonnée qui consiste à générer dans un ordre adéquat les solutions d’un problème relâché associé au problème principal jusqu’à l’obtention de la preuve d’optimalité d’une solution. Nous construisons une procédure générique dans le cadre général des problème d’optimisation combinatoire. Dans un second temps nous abordons les applications de notre algorithme sur des problèmes qui admettent le problème d’affectation comme relaxation. Le premier cas particulier que nous étudions est la recherche d’une solution de bon compromis pour le problème d’affectation multiobjectif. La seconde application se rapporte au problème du voyageur de commerce asymétrique qui présente la difficulté de comporter des contraintes qui interdisent les sous-tournées, en plus des contraintes du problème d’affectation. / Our aim in this thesis is to propose efficient algorithms for solving difficult combinatorial optimization problems. Our algorithms are based on a generic method of ordered enumeration. Initially, we describe the principle of ordered enumeration which consists in generating in a specific order solutions of a relaxed problem associated to the difficult main problem, until meeting a proof of the optimality of a feasible solution. We construct a generic procedure in the general context of combinatorial optimization problems. In a second step we discuss applications of our algorithm on some difficult problems which admit the assignment problem as relaxation. The first special case we study is the search for a compromise solution to the multiobjective assignment problem. The second application is the asymmetric travelling salesman problem, which contains sub-tour constraints in addition to the constraints of the assignment problem.

As the title already suggests the aim of the present work is to present Farkas - type results for inequality systems involving convex and/or non - convex functions. To be able to give the desired results, we treat optimization problems which involve convex and composed convex functions or non - convex functions like DC functions or fractions. To be able to use the fruitful Fenchel - Lagrange duality approach, to the primal problem we attach an equivalent problem which is a convex optimization problem. After giving a dual problem to the problem we initially treat, we provide weak necessary conditions which secure strong duality, i.e., the case when the optimal objective value of the primal problem coincides with the optimal objective value of the dual problem and, moreover, the dual problem has an optimal solution. Further, two ideas are followed. Firstly, using the weak and strong duality between the primal problem and the dual problem, we are able to give necessary and sufficient optimality conditions for the optimal solutions of the primal problem. Secondly, provided that no duality gap lies between the primal problem and its Fenchel - Lagrange - type dual we are able to demonstrate some Farkas - type results and thus to underline once more the connections between the theorems of the alternative and the theory of duality. One statement of the above mentioned Farkas - type results is characterized using only epigraphs of functions. We conclude our investigations by providing necessary and sufficient optimality conditions for a multiobjective programming problem involving composed convex functions. Using the well-known linear scalarization to the primal multiobjective program a family of scalar optimization problems is attached. Further to each of these scalar problems the Fenchel - Lagrange dual problem is determined. Making use of the weak and strong duality between the scalarized problem and its dual the desired optimality conditions are proved. Moreover, the way the dual problem of the scalarized problem looks like gives us an idea about how to construct a vector dual problem to the initial one. Further weak and strong vector duality assertions are provided.

<div>Smart warehouses have become more popular in these days, with Automated Guided Vehicles (AGVs) being used for order pickups. They also allow efficient cost management with optimized storage and retrieval. Moreover, optimization of resources in these warehouses is essential to ensure maximum efficiency. In this thesis, we consider a three dimensional smart warehouse system equipped with heterogeneous AGVs (i.e., having different speeds). We propose scheduling and placement policies that jointly consider all the different design parameters including the scheduling decision probabilities and storage assignment locations. In order to provide differentiated service levels, we propose a prioritized probabilistic scheduling and placement policy to minimize a weighted sum of mean latency and latency tail probability (LTP). Towards this goal, we first derive closed-form expressions for the mean latency and LTP. Then, we formulate an optimization problem to jointly optimize a weighted sum of both the mean latency and LTP. The optimization problem is solved efficiently over the scheduling and decision variables. For a given placement of the products, scheduling decisions of customers’ orders are solved optimally and derived in closed forms. Evaluation results demonstrate a significant improvement of our policy (up to 32%) as compared to the state of other algorithms, such as the Least Work Left policy and Join the Shortest Queue policy, and other competitive baselines.</div>

Orientador: Valeriano Antunes de Oliveira / Resumo: Neste trabalho, primeiramente, serão apresentados problemas de otimização nos quais a função objetivo é de múltiplas variáveis e de valor intervalar e as restrições de desigualdade são dadas por funcionais clássicos, isto é, de valor real. Serão dadas as condições de otimalidade usando a E−diferenciabilidade e, depois, a gH−diferenciabilidade total das funções com valor intervalar de várias variáveis. As condições necessárias de otimalidade usando a gH−diferenciabilidade total são do tipo KKT e as suficientes são do tipo de convexidade generalizada. Em seguida, serão estabelecidos problemas de controle ótimo nos quais a funçãao objetivo também é com valor intervalar de múltiplas variáveis e as restrições estão na forma de desigualdades e igualdades clássicas. Serão fornecidas as condições de otimalidade usando o conceito de Lipschitz para funções intervalares de várias variáveis e, logo, a gH−diferenciabilidade total das funções com valor intervalar de várias variáveis. As condições necessárias de otimalidade, usando a gH−diferenciabilidade total, estão na forma do célebre Princípio do Máximo de Pontryagin, mas desta vez na versão intervalar. / Abstract: In this work, firstly, it will be presented optimization problems in which the objective function is interval−valued of multiple variables and the inequality constraints are given by classical functionals, that is, real−valued ones. It will be given the optimality conditions using the E−differentiability and then the total gH−differentiability of interval−valued functions of several variables. The necessary optimality conditions using the total gH−differentiability are of KKT−type and the sufficient ones are of generalized convexity type. Next, it will be established optimal control problems in which the objective function is also interval−valued of multiple variables and the constraints are in the form of classical inequalities and equalities. It will be furnished the optimality conditions using the Lipschitz concept for interval−valued functions of several variables and then the total gH−differentiability of interval−valued functions of several variables. The necessary optimality conditions using the total gH−differentiability is in the form of the celebrated local Pontryagin Maximum Principle, but this time in the intervalar version. / Doutor