Nesta dissertação, discute-se o problema de otimização de carteiras de investimento para estratégia passiva de Index Tracking. Os objetivos principais são (i) apresentar um modelo de otimização de Index Tracking e (ii) a solucionar esse modelo com uso do método heurístico de Algoritmos Genéticos (AG) para formação de carteiras com número reduzido de ativos. O índice de referência utilizado é o Ibovespa, para o período de Janeiro/2009 a Julho/2012, com um total de 890 observações diárias de preços. A partir de uma amostra de 67 ativos, são formadas carteiras sem limite de ativos e limitadas a 40, 30, 20, 10 e 05 ativos; os intervalos de rebalanceamento das carteiras são 20, 40 e 60 períodos (dias úteis), ou seja, rebalanceamento mensal, bimestral e trimestral. É verificado que, para essa amostra, não é possível formar carteiras de 20 ou menos ativos via otimização direta com o solver Cplex com menos de 1 hora de processamento e gap abaixo de 5%. Com uso da heurística de Algoritmos Genéticos, são formadas carteiras de 10 e 05 ativos com tempo de processamento em torno de 5 minutos; nesse caso, o gap médio fica abaixo de 10% para ambos os tipos de carteira. E, com tempo de processamento do AG um pouco maior, em torno de 8 minutos, o algoritmo fornece soluções para carteiras de 10 e 05 ativos com gap médio abaixo de 5%. / In this master’s thesis it is discussed the portfolio optimization problem using the passive investment strategy of Index Tracking. The main goals are (i) to present an optimization model for the Index Tracking problem and (ii) to solve this model using the heuristic approach of Genetic Algorithms (GA) to create portfolios with reduced amount of stocks. The benchmark used is the Ibovespa Index (main reference for the Brazilian Stock Market), during the period from January/2009 to July/2012 (using a total of 890 daily stock prices). The sample contains 67 assets, and the model is used to build portfolios without limit in the amount of assets and portfolios limited to 40, 30, 20, 10 and 05 assets; the ranges of time to rebalance the portfolios are 20, 40, and 60 trading days, which means to rebalance monthly, bimonthly and quarterly. The results show that, considering this sample, it is not possible to build portfolios with 20 stocks (or less than 20) through direct optimization using the solver Cplex with computational processing time less than 1 hour and results with gap below 5%. On the other hand, using the Genetic Algorithms heuristic approach, portfolios limited to 10 and 05 stocks are built with computational time close to 5 minutes; for both types of portfolio, the solutions provided by the GA have average gap below 10%. Also, with a computational time slightly bigger, close to 8 minutes, the algorithm provides solutions with average gap below 5% for portfolios limited to 10 and 05 stocks.

Optimization plays an important role in many methods routinely used in statistics, machine learning and data science. Often, implementations of these methods rely on highly specialized optimization algorithms, designed to be only applicable within a specific application. However, in many instances recent advances, in particular in the field of convex optimization, make it possible to conveniently and straightforwardly use modern solvers instead with the advantage of enabling broader usage scenarios and thus promoting reusability. This paper introduces the R Optimization Infrastructure which provides an extensible infrastructure to model linear, quadratic, conic and general nonlinear optimization problems in a consistent way. Furthermore, the infrastructure administers many different solvers, reformulations, problem collections and functions to read and write optimization problems in various formats. / Series: Research Report Series / Department of Statistics and Mathematics

Active vibration control is a widely implemented method for the helicopter vibration control. Due to the significant progress in microelectronics, this technique outperforms the traditional passive control technique due to weight penalty and lack of adaptability for the changing flight conditions. In this thesis, an optimal controller is designed to attenuate the rotor blade vibration. The mathematical model of the triply coupled vibration of the rotating cantilever beam is used to develop the state-space model of an isolated rotor blade. The required natural frequencies are determined by the modified Galerkin method and only the principal aerodynamic forces acting on the structure are considered to obtain the elements of the input matrix. A linear quadratic regulator is designed to achieve the vibration reduction at the optimum level and the controller is tuned for the hovering and forward flight with different advance ratios.

A k-majority tournament is a directed graph that models a k-majority voting scenario, which is realized by 2k - 1 rankings, called linear orderings, of the vertices in the tournament. Every k-majority voting scenario can be modeled by a tournament, but not every tournament is a model for a k-majority voting scenario. In this thesis we show that all acyclic tournaments can be realized as 2-majority tournaments. Further, we develop methods to realize certain quadratic residue tournaments as k-majority tournaments. Thus, each tournament within these classes of tournaments is a model for a k-majority voting scenario. We also explore important structures specifically pertaining to 2- and 3-majority tournaments and introduce the idea of pseudo-3-majority tournaments and inherited 2-majority tournaments.

Substance abuse is a serious issue in both modern and traditional societies. Besides health complications such as depression, cancer and HIV, social complications such as loss of concentration, loss of job, and legal problems are among the numerous hazards substance use disorder imposes on societies. Understanding the causes of substance abuse and preventing its negative effects continues to be the focus of much research. Substance use behaviors, symptoms and signs are usually measured in form of ordinal data, which are often modeled under threshold models in Structural Equation Modeling (SEM). In this dissertation, we have developed a general nonlinear optimizer for the software package OpenMx, which is a SEM package in widespread use in the fields of psychology and genetics. The optimizer solves nonlinearly constrained optimization problems using a Sequential Quadratic Programming (SQP) algorithm. We have tested the performance of our optimizer on ordinal data and compared the results with two other optimizers (implementing SQP algorithm) available in the OpenMx package. While all three optimizers reach the same minimum, our new optimizer is faster than the other two. We then applied OpenMx with our optimization engine to a very large population-based drug abuse dataset, collected in Sweden from over one million pairs, to investigate the effects of genetic and environmental factors on liability to drug use. Finally, we investigated the reasons behind better performance of our optimizer by profiling all three optimizers as well as analyzing their memory consumption. We found that objective function evaluation is the most expensive task for all three optimizers, and that our optimizer needs fewer number of calls to this function to find the minimum. In terms of memory consumption, the optimizers use the same amount of memory.

Small satellites are becoming increasingly important to the aerospace industry mainly due to their significantly reduced development and launch cost as well as shorter development time frames. In order to meet the requirements imposed by critically limited resources of very small satellites, e.g. picosatellites, innovative approaches have to be taken in the design of effective subsystem technologies. This thesis presents the design of an active attitude determination and control system for flight testing on-board the picosatellite 'Compass-1' of the University of Applied Sciences Aachen, Germany. The spacecraft of the CubeSat class with a net spacecraft mass of only 1kg uses magnetic coils as the only means of actuation in order to satisfy operational requirements imposed by its imagery payload placed on a circular and polar Low Earth Orbit. The control system is capable of autonomously dissipating the tumbling rates of the spacecraft after launch interface separ ation and aligning the boresight of the payload into the desired nadir direction within a pointing error of approximately 10°. This nadir-pointing control is achieved by a full-state feedback Linear Quadratic Regulator which drives the attitude quaternion and their respective rates of change into the desired reference. The state of the spacecraft is determined by a static statistical QUEST attitude estimator processing readings of a three-axis magnetometer and a set of five sun sensors. Linear Floquet theory is applied to quantify the stability of the controller and a non-linear dynamics simulation is used to confirm that the attitude asymptotically converges to the reference in the absence of environmental disturbances. In the presence of disturbances the system under control suffers from fundamental underactuaction typical for purely magnetic attitude control but maintains satisfactory alignment accuracies within operational boundaries.

Thesis (Ph. D.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 29, 2007) Includes bibliographical references.

<p>In this master thesis, control algoritms using arm side sensors are investigated for an industrial robot. The sensors can be position encoders placed after the gearbox and accelerometers on the robot arms. Control strategies are discussed and evaluated for different models of the robot, after which chosen strategies are applied to a realistic model. </p><p>Control algoritms using arm side sensors (LQ, dual-loop and PD-PID) are compared to a PID-controller that only uses measurements of motor position for feedback control. The comparison are done with respect to disturbance rejection, oscillation damping, robustness and tracking performance of a reference trajectory. </p><p>Results from tests with the realistic robot modell shows that disturbance rejection was improved a factor 2 to 5 while tracking performance was improved a factor 4 to 5 according to maximum deviation from the reference path. At the same time good re-sults are achieved regarding oscillation damping and robustness.</p>

Dissertation (PhD)--University of Stellenbosch, 1995. / Bibliography. Also available via the Internet.

This work is devoted to study efficient numerical methods for solving nonsmooth convex equilibrium problems in the sense of Blum and Oettli. First we consider the auxiliary problem principle which is a generalization to equilibrium problems of the classical proximal point method for solving convex minimization problems. This method is based on a fixed point property. To make the algorithm implementable we introduce the concept of $mu$-approximation and we prove that the convergence of the algorithm is preserved when in the subproblems the nonsmooth convex functions are replaced by $mu$-approximations. Then we explain how to construct $mu$-approximations using the bundle concept and we report some numerical results to show the efficiency of the algorithm. In a second part, we suggest to use a barrier function method for solving the subproblems of the previous method. We obtain an interior proximal point algorithm that we apply first for solving nonsmooth convex minimization problems and then for solving equilibrium problems. In particular, two interior extragradient algorithms are studied and compared on some test problems.