Mecanismo Flexível é um dispositivo mecânico utilizado para transformar movimento, força ou energia entre as portas de entrada e saída sem a presença de juntas, pinos baseados em uma estrutura em monolítica, em outras palavras, a transformação do movimento é dada pela flexibilidade de sua estrutura. Deste modo a transformação pode ser direcionada em uma direção em específico, amplificando ou reduzindo o deslocamento ou força aplicados. Por este motivo mecanismos flexíveis tem grandes aplicações em micromanipulação e nano posicionamento. A concepção deste tipo de mecanismo é complexa e uma das possibilidades de elaboração deste dispositivo mecânico é através da distribuição de flexibilidade ou rigidez dentro do domínio de projeto utilizando o Método de Otimização Topológica (MOT), que essencialmente combina algoritmos de otimização numéricos como Método de Elementos Finitos (MEF), por exemplo. A grande maioria das classes de mecanismos flexíveis existentes trabalha sob pequenos deslocamentos, na ordem de micro ou nano metros, no entanto, existe uma classe de mecanismos que utiliza o recurso da flambagem não linear para operar com grandes deslocamentos. O procedimento de concepção desta de classe de mecanismo é complexa e ainda se encontra em estagio inicial, necessitando de aprimoramentos que permitam o seu projeto completo via métodos computacionais. Portanto, esta tese foi desenvolvida como objetivo desenvolver uma metodologia computacional para projetar esta classe de mecanismo flexível inovador que emprega a flambagem não linear na sua estrutura como meio para obter sob grandes deslocamentos na porta de saída. A metodologia desenvolvida se baseia no MOT para obter a topologia da estrutura que satisfaça as restrições de projeto. A modelagem do comportamento físico da estrutura utiliza uma formulação variacional não linear do problema elástico, considerando a cinemática não linear com um modelo constitutivo policonvexo. O modelo de material aplicado para obter a topologia da estrutura do mecanismo foi o Solid IsotropicMaterial with Penalization (SIMP) com um algoritmo de otimização numérico baseado no método de ponto interior, onde foi utilizada a implementação do IpOpt em conjunto com a plataforma Python FEniCS de soluções de Equações Diferenciais Parciais (EDPs). São apresentados resultados bidimensionais de mecanismos considerando algumas configurações de geometria, condições de contorno e restrições de flambagem não-linear, como incremento de carga. / The compliant mechanism is a mechanical device used to transform displacement, force or energy between the input and output ports without joints, pins based on a monolithic structure, in other words, the motion transformation is given by the flexibility of its structure. In this way the movement can be defined to a specific axis direction, amplifying or reducing the applied displacement or force. For this reason, the compliant mechanism has significant applications in micromanipulation and nanopositioning system. The design of this type of device is intricate, and one way to achieve such design is trying to distribution flexibility or rigidity within the design domain using the Topology Optimization Method (TOM), which essentially combines numerical optimization algorithms with Finite ElementMethod (FEM), for example. Most models of existing compliant mechanism work under small displacements, in the order of micro or nanometers, nevertheless, there is a class of such mechanisms that uses the nonlinear buckling behavior to operate under large displacements. The design process of this mechanism type is complicated and is still at early stages, requiring improvements that allow a complete design process via computational methods. Therefore, this thesis goal is to develop a computational methodology to create this class of innovative compliant mechanism that employs nonlinear buckling behavior to work under large displacement at the output port. The approach developed is based on TOM to achieve the optimal structure topology that satisfies the design and optimization constraints. The modeling of the elasticity behavior of the structure relies on the nonlinear variational formulation, applying the nonlinear kinematics with a polyconvex constitutive model. The SIMP is employed as a material model to obtain the optimal topology of the mechanismstructure with a numeric optimization algorithm based on the interior point method, where the IpOpt implementation was used with the high-level Python interfaces to FEniCS to solve the partial differential equations (PDEs) problem. Two-dimensional results ofmechanisms are presented considering some geometric, boundary configuration, and including nonlinear buckling as design constraints.

We report a Hessian-implicit optimization method to quickly solve the charge optimization problem over protein molecules: given a ligand and its complex with a receptor, determine the ligand charge distribution that minimizes the electrostatic free energy of binding. The new optimization couples boundary element method (BEM) and primal-dual interior point method (PDIPM); initial results suggest that the method scales much better than the previous methods. The quadratic objective function is the electrostatic free energy of binding where the Hessian matrix serves as an operator that maps the charge to the potential. The unknowns are the charge values at the charge points, and they are limited by equality and inequality constraints that model physical considerations, i.e. conservation of charge. In the previous approaches, finite-difference method is used to model the Hessian matrix, which requires significant computational effort to remove grid-based inaccuracies. In the novel approach, BEM is used instead, with precorrected FFT (pFFT) acceleration to compute the potential induced by the charges. This part will be explained in detail by Shihhsien Kuo in another talk. Even though the Hessian matrix can be calculated an order faster than the previous approaches, still it is quite expensive to find it explicitly. Instead, the KKT condition is solved by a PDIPM, and a Krylov based iterative solver is used to find the Newton direction at each step. Hence, only Hessian times a vector is necessary, which can be evaluated quickly using pFFT. The new method with proper preconditioning solves a 500 variable problem nearly 10 times faster than the techniques that must find a Hessian matrix explicitly. Furthermore, the algorithm scales nicely due to the robustness in number of IPM iterations to the size of the problem. The significant reduction in cost allows the analysis of much larger molecular system than those could be solved in a reasonable time using the previous methods. / Singapore-MIT Alliance (SMA)

We present a practical algorithm for computing the minimum volume n-dimensional ellipsoid that must contain m given points a₁,..., am ∈ Rn. This convex constrained problem arises in a variety of applied computational settings, particularly in data mining and robust statistics. Its structure makes it particularly amenable to solution by interior-point methods, and it has been the subject of much theoretical complexity analysis. Here we focus on computation. We present a combined interior-point and active-set method for solving this problem. Our computational results demonstrate that our method solves very large problem instances (m = 30,000 and n = 30) to a high degree of accuracy in under 30 seconds on a personal computer. / Singapore-MIT Alliance (SMA)

This thesis aims to study the theoretical complexity and empirical performance of decomposition algorithms. We focus on linear programs with a block-angular structure. Decomposition algorithms used to be the only way to solve large-scale special structured problems, in terms of memory limit and CPU time. However, with the advances in computer technology over the past few decades, many large-scale problems can now be solved simply by using some general purpose LP software, without exploiting the problems' inner structures. A question arises naturally, should we solve a structured problem with decomposition, or directly solve it as a whole? We try to understand how a problem's characteristics influence its computational performance, and compare the relative efficiency of algorithms with and without decomposition. Two comparisons are conducted in our research: first, the Dantzig-Wolfe decomposition method (DW) versus the simplex method (simplex); second, the analytic center cutting plane method (ACCPM) versus the interior point method (IPM). These comparisons fall into the two main solution approaches in linear programming: simplex-based algorithms and IPM-based algorithms. Motivated by our observations of ACCPM and DW decomposition, we devise a hybrid algorithm combining ACCPM and DW, which are the counterparts of IPM and simplex in the decomposition framework, to take the advantages of both: the quick convergence rate of IPM-based methods, as well as the accuracy of simplex-based algorithms. A large set of 316 instances is incorporated in our experiments, so that different dimensioned problems with primal or dual block-angular structures are covered to test our conclusions.

This thesis aims to study the theoretical complexity and empirical performance of decomposition algorithms. We focus on linear programs with a block-angular structure. Decomposition algorithms used to be the only way to solve large-scale special structured problems, in terms of memory limit and CPU time. However, with the advances in computer technology over the past few decades, many large-scale problems can now be solved simply by using some general purpose LP software, without exploiting the problems' inner structures. A question arises naturally, should we solve a structured problem with decomposition, or directly solve it as a whole? We try to understand how a problem's characteristics influence its computational performance, and compare the relative efficiency of algorithms with and without decomposition. Two comparisons are conducted in our research: first, the Dantzig-Wolfe decomposition method (DW) versus the simplex method (simplex); second, the analytic center cutting plane method (ACCPM) versus the interior point method (IPM). These comparisons fall into the two main solution approaches in linear programming: simplex-based algorithms and IPM-based algorithms. Motivated by our observations of ACCPM and DW decomposition, we devise a hybrid algorithm combining ACCPM and DW, which are the counterparts of IPM and simplex in the decomposition framework, to take the advantages of both: the quick convergence rate of IPM-based methods, as well as the accuracy of simplex-based algorithms. A large set of 316 instances is incorporated in our experiments, so that different dimensioned problems with primal or dual block-angular structures are covered to test our conclusions.

In this thesis modelling and solution methods for portfolio optimisation are presented. The investigations reported in this thesis extend the Markowitz mean-variance model to the domain of quadratic mixed integer programming (QMIP) models which are 'NP-hard' discrete optimisation problems. In addition to the modelling extensions a number of challenging aspects of solution algorithms are considered. The relative performances of sparse simplex (SSX) as well as the interior point method (IPM) are studied in detail. In particular, the roles of 'warmstart' and dual simplex are highlighted as applied to the construction of the efficient frontier which requires processing a family of problems; that is, the portfolio planning model stated in a parametric form. The method of solving QMIP models using the branch and bound algorithm is first developed; this is followed up by heuristics which improve the performance of the (discrete) solution algorithm. Some properties of the efficient frontier with discrete constraints are considered and a method of computing the discrete efficient frontier (DEF) efficiently is proposed. The computational investigation considers the efficiency and effectiveness in respect of the scale up properties of the proposed algorithm. The extensions of the real world models and the proposed solution algorithms make contribution as new knowledge.

A surface reconstruction and image enhancement non-linear finite element technique based on minimization of L1 norm of the total variation of the gradient is introduced. Since minimization in the L1 norm is computationally expensive, we seek to improve the performance of this algorithm in two fronts: first, local L1- minimization, which allows parallel implementation; second, application of the Augmented Lagrangian method to solve the minimization problem. We show that local solution of the minimization problem is feasible. Furthermore, the Augmented Lagrangian method can successfully be used to solve the L1 minimization problem. This result is expected to be useful for improving algorithms computing digital elevation maps for natural and urban terrain, fitting surfaces to point-cloud data, and image super-resolution.

Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-08-24Bitstream added on 2014-06-13T18:07:18Z : No. of bitstreams: 1 homem_tpd_me_bauru.pdf: 3557697 bytes, checksum: a1fa6fe9ed118fd4c4f8be6400b6d78f (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O Brasil é o maior produtor de cana-de-açúcar do mundo. Mas, existe uma grande preocupação com o sistema de colheita utilizado nesta cultura, pois é prática comum a colheita manual com a pré-queima do palhiço. Autoridades brasileiras têm aprovado leis proibindo a queimada nos canaviais. Entretanto, a colheita mecanizada, com cana-de-açúcar crua, cria novos problemas com a permanência do resíduo no solo. Assim, muitos estudos têm sido propostos para o uso deste resíduo para geração de energia. A maior dificuldade no uso desta biomassa está no custo de coletar e transferir o resíduo, do campo para o centro de processamento. Para análise da viabilidade deste sistema há a necessidade de um estudo do balanço de energia envolvido, devido ao grande número de maquinário utilizado no processo. O objetivo deste trabalho é investigar modelos matemáticos que auxiliem na escolha das variedades de cana-de-açúcar a serem implantadas, de forma a minimizar o custo de coleta da biomassa residual e avaliar o balanço de energia gerado, adicionado restrições sobre a produção de sacarose e limitações da área para plantio e considerando as distâncias entre os talhões e o centro de processamento. Para isto, técnicas de programação linear e inteira 0-1 foram utilizadas. A busca de soluções para problemas de programação inteira com grande número de variáveis e restrições é de difícil resolução, mas os resultados apresentados mostram que a utilização d eum procedimento híbrido envolvendo o método Primal-Dual de Pontos Interiores e o método Branch and Bound promove uma boa performance computacional, apresentando soluções confiáveis. Assim, o uso deste procedimento é viável para o auxílio na seleção de variedades, otimizando o custo do uso da biomassa residual de colheita ou o balanço de geração de energia / It is that Brazil is the world's largest sugar cane producer. But there is great concern about the harvesting system used in this culture, because it is a common practice to burn the straw before the barvest. Brazilian authorities have approved laws prohibiting the burning in the sugar cane fields. However, with mechanized harvesting of sugar cane raw creates new problems with the accumulation of the waste biomass in the ground. Many studies have been proposed to use this waste for energy generation. The greatest difficulty to use this biomass is in the cost of collect and transfer the residues from the field to the the processing center. To analyze the feasibility of this system, it is necessary a study of the involved energy balance, because of the large number of machines in the process. The aim of this study is to investigate mathematical models that help on choosing varieties of sugar cane to be planted, to minimize the cost of collect of residual biomass and to analyze the balance of power generated, adding restrictions on the production on the production of sucrose and limitations on the area for planting and considering the distances among the plots the processing center. To this, techniques of 0-1 integer linear programming were used. The search for solutions to integer programming problems with many variables and constraints its very hard, but the results show that the use of a hybrid procedure involving the Primal-Dual Interior Point method and Branch and Bound method promotes good performance computing, with reliable solutions. Thus, the use of this procedure is feasible to help on select of varieties, optimizing the cost of collect of the waste biomass or the the balance of power generation

The main topic of this thesis is model predictive control (MPC) of an unstable fighter aircraft. When flying it is important to be able to reach, but not exceed the aircraft limitations and to consider the physical boundaries on the control signals. MPC is a method for controlling a system while considering constraints on states and control signals by formulating it as an optimization problem. The drawback with MPC is the computational time needed and because of that, it is primarily developed for systems with a slowly varying dynamics. Two different methods are chosen to speed up the process by making simplifications, approximations and exploiting the structure of the problem. The first method is an explicit method, performing most of the calculations offline. By solving the optimization problem for a number of data sets and thereafter training a neural network, it can be treated as a simpler function solved online. The second method is called fast MPC, in this case the entire optimization is done online. It uses Cholesky decomposition, backward-forward substitution and warm start to decrease the complexity and calculation time of the program. Both methods perform reference tracking by solving an underdetermined system by minimizing the weighted norm of the control signals. Integral control is also implemented by using a Kalman filter to observe constant disturbances. An implementation was made in MATLAB for a discrete time linear model and in ARES, a simulation tool used at Saab Aeronautics, with a more accurate nonlinear model. The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.

Mecanismo Flexível é um dispositivo mecânico utilizado para transformar movimento, força ou energia entre as portas de entrada e saída sem a presença de juntas, pinos baseados em uma estrutura em monolítica, em outras palavras, a transformação do movimento é dada pela flexibilidade de sua estrutura. Deste modo a transformação pode ser direcionada em uma direção em específico, amplificando ou reduzindo o deslocamento ou força aplicados. Por este motivo mecanismos flexíveis tem grandes aplicações em micromanipulação e nano posicionamento. A concepção deste tipo de mecanismo é complexa e uma das possibilidades de elaboração deste dispositivo mecânico é através da distribuição de flexibilidade ou rigidez dentro do domínio de projeto utilizando o Método de Otimização Topológica (MOT), que essencialmente combina algoritmos de otimização numéricos como Método de Elementos Finitos (MEF), por exemplo. A grande maioria das classes de mecanismos flexíveis existentes trabalha sob pequenos deslocamentos, na ordem de micro ou nano metros, no entanto, existe uma classe de mecanismos que utiliza o recurso da flambagem não linear para operar com grandes deslocamentos. O procedimento de concepção desta de classe de mecanismo é complexa e ainda se encontra em estagio inicial, necessitando de aprimoramentos que permitam o seu projeto completo via métodos computacionais. Portanto, esta tese foi desenvolvida como objetivo desenvolver uma metodologia computacional para projetar esta classe de mecanismo flexível inovador que emprega a flambagem não linear na sua estrutura como meio para obter sob grandes deslocamentos na porta de saída. A metodologia desenvolvida se baseia no MOT para obter a topologia da estrutura que satisfaça as restrições de projeto. A modelagem do comportamento físico da estrutura utiliza uma formulação variacional não linear do problema elástico, considerando a cinemática não linear com um modelo constitutivo policonvexo. O modelo de material aplicado para obter a topologia da estrutura do mecanismo foi o Solid IsotropicMaterial with Penalization (SIMP) com um algoritmo de otimização numérico baseado no método de ponto interior, onde foi utilizada a implementação do IpOpt em conjunto com a plataforma Python FEniCS de soluções de Equações Diferenciais Parciais (EDPs). São apresentados resultados bidimensionais de mecanismos considerando algumas configurações de geometria, condições de contorno e restrições de flambagem não-linear, como incremento de carga. / The compliant mechanism is a mechanical device used to transform displacement, force or energy between the input and output ports without joints, pins based on a monolithic structure, in other words, the motion transformation is given by the flexibility of its structure. In this way the movement can be defined to a specific axis direction, amplifying or reducing the applied displacement or force. For this reason, the compliant mechanism has significant applications in micromanipulation and nanopositioning system. The design of this type of device is intricate, and one way to achieve such design is trying to distribution flexibility or rigidity within the design domain using the Topology Optimization Method (TOM), which essentially combines numerical optimization algorithms with Finite ElementMethod (FEM), for example. Most models of existing compliant mechanism work under small displacements, in the order of micro or nanometers, nevertheless, there is a class of such mechanisms that uses the nonlinear buckling behavior to operate under large displacements. The design process of this mechanism type is complicated and is still at early stages, requiring improvements that allow a complete design process via computational methods. Therefore, this thesis goal is to develop a computational methodology to create this class of innovative compliant mechanism that employs nonlinear buckling behavior to work under large displacement at the output port. The approach developed is based on TOM to achieve the optimal structure topology that satisfies the design and optimization constraints. The modeling of the elasticity behavior of the structure relies on the nonlinear variational formulation, applying the nonlinear kinematics with a polyconvex constitutive model. The SIMP is employed as a material model to obtain the optimal topology of the mechanismstructure with a numeric optimization algorithm based on the interior point method, where the IpOpt implementation was used with the high-level Python interfaces to FEniCS to solve the partial differential equations (PDEs) problem. Two-dimensional results ofmechanisms are presented considering some geometric, boundary configuration, and including nonlinear buckling as design constraints.