[pt] Neste trabalho, quatro métodos projetados especificamente para a estimação de sistemas esparsos são originalmente elaborados e apresentados. São eles: Encolhimentos Sucessivos, Expansões Sucessivas, Minimização da Norma l1 e Ajuste Automático do fator de regularização do Custo LS. Os quatro métodos propostos baseiam-se na técnica de estimação de sistemas lineares e invariantes no tempo pelo critério dos mínimos quadrados, universalmente conhecida por sua denominação em inglês - Least Squares (LS) Estimation, e incorporam técnicas relacionadas a otimização convexa e à teoria de compressive sensing. Os resultados obtidos em simulações mostram que os métodos em questão têm desempenho superior que a estimação LS convencional e que o algoritmo Recursive Least Squares (RLS) com regularização convexa denominado l1-RLS, em muitos casos alcançando o desempenho ótimo apresentado pelo método de estimação LS Oráculo, no qual o suporte da resposta ao impulso em tempo discreto do sistema estimado é conhecido a priori. Além disso, os métodos propostos apresentam custo computacional menor que do algoritmo l1-RLS. / [en] In this thesis, four methods specifically designed for sparse systems estimation are originally developed and presented, which were called here: Relaxations method, Successive Expansions method, l1-norm Minimization method and Automatic Adjustment of the Regularization Factor method. The four proposed methods are based on the Least Squares (LS) Estimation method and incorporate techniques related to convex optimization and to the theory of compressive sensing. The simulation results show that the proposed methods herein present superior performance than the ordinary LS estimation method and the Recursive Least Squares (RLS) with convex regularization method (l1-RLS), in many cases achieving the same optimal performance presented by the LS Oracle method. Furthermore, the proposed methods demand lower computational cost than the l1-RLS method.

Orientador: Michel Daoud Yacoub / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-10T01:21:53Z (GMT). No. of bitstreams: 1 CaceresZuniga_YusefRafael_D.pdf: 1196886 bytes, checksum: b589961266e398a3fd22bfd7b30719e4 (MD5) Previous issue date: 2007 / Resumo: Nesta tese, estuda-se o problema da alocação de potência através de duas abordagens: estocástica e robusta, sendo os ganhos do canal, que descrevem o estado do sistema de comunicações sem fio, parcialmente observados pelo decisor. Na abordagem estocástica, considera-se que os ganhos do canal são variáveis aleatórias, que representam a variação rápida do sinal de rádio. Nesse contexto, reformula-se o índice de desempenho do sistema através do CVaR (Conditional. Value-at-Risk). Na abordagem robusta, considera-se que os ganhos do canal e o ruído pertencem a um determinado conjunto convexo. Em ambas as abordagens, a solução ótima é obtida em termos de um problema de otimização convexa. Adicionalmente, na abordagem estocástica, apresenta-se um algoritmo recursivo e distribuído, que converge para uma solução subótima, quando o ruído é nulo e a potência transmitida é limitada tanto superior como inferiormente. Também mostra-se que, em um sistema onde os ganhos do canal coincidem com o seu valor esperado, esse algoritmo converge para a soluçãã ótima quando a qualidade do enlace é muito maior que a mínima requerida / Abstract: This thesis deals with the power allocation problem under the stochastic and robust approaches, where the channel gains describe the wireless communication system state and are partially known by the controller. The stochastic approach considers the channel gains as random variables which represent the fast fading of the radio signal. Under these settings, the system performance index is reformulated using CVaR (Conditional Value-at-Risk). The robust approach considers that the channels gains and noise belong to a determined convex set. ln both approaches, the optimal solution is determined in terms of a convex optimization problem. Additionally, under the stochastic approach, a recursive and distributed algorithm is presented which converges to its suboptimal solution when noise is null and the transmitted power is upper and lower bounded. It is also show that this algorithm converges to its optimal solution when the link quality is much greater than the minimum required quality in a system where the channels gains match its expected value / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

Cette thèse consiste à étudier l'organisation du système de contrôle des voies métaboliques des bactéries afin de dégager des propriétés systémiques révélant son fonctionnement. Dans un premier temps, nous montrons que le contrôle des voies métaboliques est hautement structuré et peut se décomposer en modules fortement découplés en régime stationnaire. Ces modules possèdent des propriétés mathématiques remarquables ayant des conséquences importantes en biologie. Cette décomposition, basée intrinsèquement sur la vision système de l'Automatique, offre un cadre théorique formel général d'analyse du contrôle des voies métaboliques qui s'est révélé effectif pour analyser des données expérimentales. dans un deuxième temps, nous nous intéressons aux raisons possibles de l'émergence de cette structure de contrôle similaire. Nous identifions un ensemble de contraintes structurelles agissant au niveau de la répartition d'une ressource commune, les protéines, entre les processus cellulaires. Respecter ces contraintes pour un taux de croissance donné conduit à formaliser et résoudre un problème d'optimisation convexe non différentiable, que nous appelons Resource balance Analysis. Ce problème d'optimisation se résout numériquement à l'échelle de la bactérie grâce à un problème de Programmation Linéaire équivalent. plusieurs propriétés sont déduites de l'analyse théorique du critère obtenu. Tout d'abord, le taux de croissance est structurellement limité par la répartition d'une quantité finie de protéines entre les voies métaboliques et les ribosomes. Ensuite, l'émergence des modules dans les voies métaboliques provient d'une politique générale d'économie en protéines chez la bactérie pour gagner du taux de croissance. Certaines stratégies de transport bien connues comme la répression catabolique ou la substitution de transporteurs haute/basse affinités sont prédites par notre méthode et peuvent alors être interprétées comme le moyen de maximiser la croissance tout en minimisant l'investissement en protéines. / This thesis consist in studying the organization of the control system of metabolic pathways of bacteria to identify systemic properties revealing its operation. At first, we show that control of metabolic pathways is highly structured and can be decomposed into modules strongly decoupled in steady-state. These modules are defined by their singular mathematical properties having important implications in biology. This decomposition, based inherently on the system outlook of automatic control, offers a formal theoretical analysis of general control of metabolic pathways, which has been effective in analysing experimental data. In a second step, we consider the possible reasons for the emergence of this modular control structure. We identify a set of structural constraints acting at the distribution of a common resourc, the proteins between cellular processes. Satisfying these constraints for a given growth rate leads to formalize and to solve a non-differentiable convex optimization problem, that we call Resource Balance Analysis. This optimization problem is solved numerically at the scale of the bacteria through an equivalent linear programming problem. Several properties are derived from theoretical analysis of the obtained criterion. Firts, the growth rate is structurally limited by the distribution of a finite amount of proteines between the metabolic pathways and the ribosomes. Second, the emergence of modules in metabolic pathways arises from a policy of economy in proteins in the bacterium to increase the growth rate. Some well known transport strategies such as catabolite repression of the substitution between low/highaffinity transporters are predicted by our methods and could consequently be interpretd as ways to maximize growth while minimizing investment in proteins.

No description available.

<div>Optimization of atmospheric flight control has long been performed on the ground, prior to mission flight due to large computational requirements used to solve non-linear programming problems. Onboard trajectory optimization enables the creation of new reference trajectories and updates to guidance coefficients in real time. This thesis summarizes the methods involved in solving optimal control problems in real time using convexification and Sequential Convex Programming (SCP). The following investigation provided insight in assessing the use of state of the art SCP optimization architectures and convexification of the hypersonic equations of motion[ 1 ]–[ 3 ] with different control schemes for the purposes of enabling on-board trajectory optimization capabilities.</div><div>An architecture was constructed to solve convexified optimal control problems using direct population of sparse matrices in triplet form and an embedded conic solver to enable rapid turn around of optimized trajectories. The results of this show that convexified optimal control problems can be solved quickly and efficiently which holds promise in autonomous trajectory design to better overcome unexpected environments and mission parameter changes. It was observed that angle of attack control problems can be successfully convexified and solved using SCP methods. However, the use of multiple coupled controls is not guaranteed to be successful with this method when they act in the same plane as one another. The results of this thesis demonstrate that state of the art SCP methods have the capacity to enable onboard trajectory optimization with both angle of attack control and bank angle control schemes.</div><div><br></div>

The master's thesis deals with the problem of restoring a block of missing samples in a digital audio signal. This problem is formulated as an optimization task, which seeks the sparsest time-frequency representation of a signal within the set of feasible reconstructed signals. Several particular formulations are discussed, namely the analyzing and the synthesizing model, both for convex and non-convex approaches. Suitable algorithms are proposed for solving these formulations, and in the convex case, the method is further enhanced by various procedures to compensate for the energy drop in the inpainted signal segment. The proposed algorithms are tested on real recordings, and their performance is shown to be competitive with the state-of-the-art.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

No description available.

The aim of this work is to make some investigations concerning duality for multiobjective optimization problems. In order to do this we study first the duality for scalar optimization problems by using the conjugacy approach. This allows us to attach three different dual problems to a primal one. We examine the relations between the optimal objective values of the duals and verify, under some appropriate assumptions, the existence of strong duality. Closely related to the strong duality we derive the optimality conditions for each of these three duals. By means of these considerations, we study the duality for two vector optimization problems, namely, a convex multiobjective problem with cone inequality constraints and a special fractional programming problem with linear inequality constraints. To each of these vector problems we associate a scalar primal and study the duality for it. The structure of both scalar duals give us an idea about how to construct a multiobjective dual. The existence of weak and strong duality is also shown. We conclude our investigations by making an analysis over different duality concepts in multiobjective optimization. To a general multiobjective problem with cone inequality constraints we introduce other six different duals for which we prove weak as well as strong duality assertions. Afterwards, we derive some inclusion results for the image sets and, respectively, for the maximal elements sets of the image sets of these problems. Moreover, we show under which conditions they become identical. A general scheme containing the relations between the six multiobjective duals and some other duals mentioned in the literature is derived. / Das Ziel dieser Arbeit ist die Durchführung einiger Untersuchungen bezüglich der Dualität für Mehrzieloptimierungsaufgaben. Zu diesem Zweck wird als erstes mit Hilfe des so genannten konjugierten Verfahrens die Dualität für skalare Optimierungsaufgaben untersucht. Das erlaubt uns zu einer primalen Aufgabe drei unterschiedliche duale Aufgaben zuzuordnen. Wir betrachten die Beziehungen zwischen den optimalen Zielfunktionswerten der drei Dualaufgaben und untersuchen die Existenz der starken Dualität unter naheliegenden Annahmen. Im Zusammenhang mit der starken Dualität leiten wir für jede dieser Dualaufgaben die Optimalitätsbedingungen her. Die obengenannten Ergebnisse werden beim Studium der Dualität für zwei Vektoroptimierungsaufgaben angewandt, und zwar für die konvexe Mehrzieloptimierungsaufgabe mit Kegel-Ungleichungen als Nebenbedingungen und für eine spezielle Quotientenoptimierungsaufgabe mit linearen Ungleichungen als Nebenbedingungen. Wir assoziieren zu jeder dieser vektoriellen Aufgaben eine skalare Aufgabe für welche die Dualität betrachtet wird. Die Formulierung der beiden skalaren Dualaufgaben führt uns zu der Konstruktion der Mehrzieloptimierungsaufgabe. Die Existenz von schwacher und starker Dualität wird bewiesen. Wir schliessen unsere Untersuchungen ab, indem wir eine Analyse von verschiedenen Dualitätskonzepten in der Mehrzieloptimierung durchführen. Zu einer allgemeinen Mehrzieloptimierungsaufgabe mit Kegel-Ungleichungen als Nebenbedingungen werden sechs verschiedene Dualaufgaben eingeführt, für die sowohl schwache als auch starke Dualitätsaussagen gezeigt werden. Danach leiten wir verschiedene Beziehungen zwischen den Bildmengen, bzw., zwischen den Mengen der maximalen Elemente dieser Bildmengen der sechs Dualaufgaben her. Dazu zeigen wir unter welchen Bedingungen werden diese Mengen identisch. Ein allgemeines Schema das die Beziehungen zwischen den sechs dualen Mehrzieloptimierungsaufgaben und andere Dualaufgaben aus der Literatur enthält, wird dargestellt.

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.